Road Extraction Using Spectral Mixture And Matched ... - CiteSeerX

Road Extraction Using Spectral Mixture And Matched Filter Techniques Dar Roberts, Meg Gardner, and Chris Funk UCSB

Introduction Spatially accurate and up-to-date road networks are important for numerous reasons. However, these databases do not exist for vast areas, particularly in areas with rapid expansion. Remote sensing provides one means by which large areas may be mapped with a high standard of accuracy. The goal of our research is to develop accurate road extraction techniques using high spatial resolution, fine spectral resolution imagery. We also aim to define the spatial and spectral requirements for remote sensing data to be used successfully for road feature extraction. Research Our research focused on two primary objectives, development of a regionally specific spectral library for urban areas, and using advanced techniques to map urban materials from hyperspectral data. Our initial focus has been on the use of high resolution Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) data acquired over Santa Barbara, California, in October 1999. AVIRIS samples between 0.37 and 2.5 microns in 224 spectral bands, providing detailed and continuous reflectance information in this region. The spatial resolution for these data is approximately 3.9 meters. This data set was selected because it covers a wide range of surface materials (industrial, residential, agricultural, barren, and natural areas and a wide assortment of road types) at a fine enough spatial resolution to be used in urban areas. The large number of bands and fine spatial resolution also make it possible to synthesize most major broad-band systems, including SPOT, Landsat TM, Ikonos multispectral data etc. Development of the Urban Spectral Library A regionally specific urban spectral library was developed by extracting 3.9 meter resolution spectra from the 1999 AVIRIS flight over Santa Barbara. Our initial objective was to develop a first generation library that included high quality spectra of many urban materials. To retrieve surface reflectance from AVIRIS, we used an algorithm developed by Robert Green (Green et al. 1993; 1996), that fits radiance, modeled using Modtran radiative transfer code to radiance measured from AVIRIS. A field target was used to remove artifacts from AVIRIS reflectance due to a variety of sources, including AVIRIS wavelength calibration and errors in MODTRAN. A total of over 100 spectra were extracted for the first generation urban spectral library (Figure 1). To accomplish this, distinct cover types were first identified in the field then spectra were extracted from the image. Spectra were averaged over range of pixels depending on the size of the target. Once extracted from the image each spectrum was given a unique identifier in the spectral library. Metadata, describing the location and type of material were recorded in an excel spread sheet.

Reflectance (500=50%)

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Rf1_whmet Rd3_asph Soil4 Veg4_grass Rf23_wood Rf40_comp Rd7_conc

500 400 300 200 100 0 350

850

1350 1850 Wavelength (nm)

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Figure 1. Example Library Spectra. Rf1_whmet=white metal roof; Rd3_asph=asphalt road; Soil4=bare soil; Veg4_grass=irrigated grass; Rf23_wood=wood roof; Rf40_comp=composite shingle roof; Rd7_conc=concrete road

Mapping Urban Materials Urban materials were mapped using a technique developed at UCSB called Multiple Endmember Spectral Mixture Analysis (MESMA). Spectral Mixture Analysis is a technique designed to decompose spectra, acquired from "mixed" targets (more than one material within the Instantaneous Field of View of the instrument) into "fractions" of pure spectra, called endmembers. This technique, while well suited for natural areas, is inappropriate for urban areas where the number of unique spectra greatly exceeds the 3 to 4 endmembers typically used. MESMA departs from the simple model in that it allows the number and type of endmembers to vary on a per pixel basis, and thus has the potential of mapping hundreds of unique materials. For this analysis endmembers derived from the urban spectral library were coupled with shade and used to unmix the 1999 Santa Barbara AVIRIS data. The final product is a set of maps, one showing the model selected (essentially a land-cover map), two or more images showing abundance and a final image showing model error. Initial results from MESMA were promising, showing that the technique is capable of mapping a large number of urban surfaces, including roads. However, the initial results also demonstrated significant confusion between some road surfaces and dark composite shingles. Obstacles and New Directions For this research, the most significant obstacles lies in the spectral confusion between some composite roof material and some road types. This confusion occurs because many roofs and roads are composed of the same basic materials. One approach we are evaluating is the combined application of MESMA and spatial pattern recognition techniques. For example, while road surfaces and composite roofs may appear spectrally similar, their spatial patterns should be unique - roads are likely to form linear features,

while roofs are likely to be more rectangular. Because both the spectral library and urban materials map are considered first generation products, another significant direction will be in further refinements to the spectral library. Once we have successfully extracted road features using high-resolution AVIRIS, we will begin determining the minimum spectral and spatial requirements for mapping urban materials. We will do this by degrading the spatial resolution of the high resolution AVIRIS data. To evaluate broad band systems such as Landsat TM or SPOT, we will convolve AVIRIS wavelengths to the equivalent broad-band spectra. This will allow us to better define the imagery requirements—and therefore cost—for the technique to be used on the large scale.

Q-Tree contiguity filters to improve raster/vector conversions: Detecting transportation objects in fine resolution urban and coarse resolution rural imagery Quarterly Report Chris Funk Introduction The technique described below addresses at least two different transportation applications. First, at the urban scale, our partners at Iowa State University have, in cooperation with the Iowa DOT, identified the following information requirements through a user needs assessment (UNA): 1) The number and location of intersection features, such as stopbars and turn lanes. 2) Features controlling highway access including driveway density and spacing, as well as median thickness and openings. 3) The location and status of roadway inventory data such as street stripings and parking lot spaces. Second, the consortium as a whole has been advised to give high priority to techniques that can rapidly and inexpensively generate national street centerline databases. These techniques are urgently required in the United States, where mapping accuracies are as bad as ±200m in some areas. They are also applicable in other countries where the state of centerline databases is much less enviable. In many cases there are simply no records of roads in rural areas, and this makes delivery of routine and emergency services difficult. Remotely sensed datasets exist which can address user needs at both these scales. i)

Coarse resolution Thematic Mapper imagery is available at continental scales, for all continents, at very reasonable rates. This acquisition of this data has been a major accomplishment of the earth observing system.

ii)

Fine-resolution elevation and pan-chromatic data produced using the University of Florida's Airborne Laser Swath Mapping System (ALSM) can be analyzed to reveal the features identified by the Iowa DOT’s UNA.

The methods described here can mine this data, helping to identify roads in rural areas and higer resolution features in urban settings.

Technique Rster to vector conversion tanslates remotely sensed imagery into useable information. If images contain a lot of background noise – pixels of similar intensity, but unconnected to the features of interest, then vectorizing can be a laborious, involved process, requiring considerable human interaction. If, on the other hand, the image has been pre-filtered to remove these ‘noise’ pixels, then existing commercial software packages can vectorize images at close to a button click. The Q-Tree method described below is one way to identify and remove noise pixels. It can be applied to (i) finding roads in forests using coarse scale imagery, (ii) finding lane stripes, pedestrian crossings and other objects of interest on fine scale images, as well as feature identification task using other types of imagery.

It should be noted that noise is scale-dependent, and depends on both the heterogeneity of the background and the clarity of the desired features. So for example, it may be possible to identify roadways within 30m pixels, against a relatively smooth (and different) background, such as in the American prairies or the Amazonian rainforests. At the same 30m resolution, urban areas degrade into ‘stone soup’, with building and streets strongly confounding in most pixels. Each application, therefore, has its applicable scale. The Q-Tree method can operate on all of them, and differentiate connected from unconnected pixels.

The Q-Tree Filter The ‘Q’ stands for spatial query; the tree represents an answer to the question: which pixels are connected to this one? The Q-Tree filter works by partially vectorizing an image and selecting out those pixels which are connected to a set number of neighboring pixels. In other words, it translates a complicated image into a simple binary mask, identifying only the pixels of interest. The process begins by specifying a target spectra, such as a white line or a brown roadway. Then at each pixel the ‘spectral distance’ between the pixel and the desired target is calculated. This spectral distance is the Cartesian distance, calculated using the radiance values. So for example, the spectral distance between a (p)ixel and a (t)arget spectra in the pan-chromatic images used in the examples described below is just:

d p ,t = ( red p − redt ) 2 + ( greenp − greent ) 2 + (blue p − bluet ) 2 This calculation, of course, can be extended to higher dimensions, accommodating multi and hyper-spectral data types. A C program has been written to perform this measurement and build a dynamic tree structure to represent the relationship between each pixel and its neighbors (Code listing in Appendix A). Trees are data structure which can represent topological relationships. They contain nodes and links. Each node represents a connected pixel, and links the connections between them. We begin by defining a first node, the pixel of interest, which we can label a (see Figure 1). The spectral distance between this pixel and its neighbors is then calculated. If neighboring pixels fall within a user-defined spectral distance, then they are marked as neighbors and new nodes are added to the growing Q-Tree. This process is repeated for each new node in the Q-Tree, and the filter grows until no new neighbors are found or a user-defined maximum level of recursion is reached. The process returns the total number of connected pixels (i.e. the total number of nodes in the QTree). This value is assigned to the same location in a new 1-byte image. A new Q-Tree is grown for each pixel in the original image. This generates a filtered image, where each pixel has assigned to it the number of linked target pixels touching that location. Thresholding this image generates a binary (0/1) image that is much simpler than the original, and is much more suitable for processing with automated raster-to-vector software.

Discussion/Conclusions We have performed two simple demonstrations of this method, using pan-chromatic imagery representing i) a fine resolution scene of an urban intersection, and ii) a simulated coarse resolution image of a tropical rainforest. In the first scene we use the Q-Tree filter to isolate contiguous white pixels associated with lanes, stopbars street lines and turn arrows (figure 2). The filtered image (right hand panel in figure 2), while not perfect, comes close to isolating many of the information features specified as needs by users. This filtering process may be used on data of any resolution, as we demonstrate by filtering a roadway from a coarseresolution pan-chromatic image (figure 3). This image was generated by selecting heavily forested areas from the high resolution imagery obtained by the University of Florida's Airborne Laser Swath Mapping System (ALSM). This ‘rainforest’ was then degraded to an approximately 30m resolution by averaging neighboring pixels. The Q-Tree filter was then used to extract strongly linked ‘road pixels’. These initial results are highly encouraging. They will be extended in the future to include more sophisticated spectral and spatial measures of suitability.

Figure 1: Q-Tree example. A simple image is shown on the left, and the corresponding Q-Tree for the node a is shown on the right. The construction of the Q-Tree begins with the addition of the node aa. This node has two offspring aaa and aab. Each of these has one node added (aaaa to aaa, and aaba to aab). In the last level of recursion, aaaaa is added to aaaa, while aaba receives aabaa and aabab. The total number of nodes in this Q-Tree (9) is then assigned to the pixel location marked a.

Figure 2: Q-Tree filter applied in urban setting. On the left is a pan-chromatic image acquired by the University of Florida's Airborne Laser Swath Mapping System (ALSM). On the right is the image after filtering by the Q-Tree.

Figure 3: Q-Tree filter applied in a rural setting. On the left is a simulated rainforest, based on pan-chromatic image acquired by the University of Florida's Airborne Laser Swath Mapping System (ALSM). Forest subsections of the high resolution pan-chromatic image were degraded to approximate a 30m resolution image (left panel). The Q-Tree filter was applied to this image, pulling out the roadway, and suppressing bare patches of soil.

Appendix A: Q-Tree.c * * Q-Tree.c * */ #include #include #include #include "gd.h" #include "gdfontg.h" #include "gdfonts.h" typedef long Count; Count node0x; Count node0y; /** ughh, cheesy global variables **/ typedef struct { void * nodeU; void * nodeD; void * nodeL; void * nodeR; Count x, y; } QNODE;

void freeTree( QNODE * node , gdImagePtr im, unsigned char * usedIDs) { usedIDs[(im->sx*node->y)+node->x] = 0; if( node->nodeU ) freeTree( (QNODE *) node->nodeU , im, usedIDs ); if( node->nodeD ) freeTree( (QNODE *) node->nodeD , im, usedIDs ); if( node->nodeL ) freeTree( (QNODE *) node->nodeL , im, usedIDs ); if( node->nodeR ) freeTree( (QNODE *) node->nodeR , im, usedIDs ); }

QNODE * testFlow( im_in, oldX, oldY, newX, newY, targetRed, targetGreen, targetBlue, maxDistance ) gdImagePtr im_in; Count oldX, oldY, newX, newY; float targetRed, targetGreen, targetBlue, maxDistance; { QNODE * qnode; int c; float dist, oldDist; if( newX < 0 ) return 0x0; if( newX >= im_in->sx ) return 0x0; if( newY < 0 ) return 0x0;

if( newY >= im_in->sy) return 0x0; c

= gdImageGetPixel( im_in, newX, newY );

/** test color dist **/ dist = ((float)im_in->red[c] -targetRed) *((float)im_in->red[c] -targetRed) + ((float)im_in->green[c]-targetGreen)*((float)im_in->green[c]-targetGreen) + ((float)im_in->blue[c] -targetBlue) *((float)im_in->blue[c] -targetBlue) ; if( dist > maxDistance ) return 0x0; /** test spatial dist **/ oldDist = ((float)(oldX-node0x)*(float)(oldX-node0x))+((float)(oldY-node0y)*(float)(oldY-node0y)); dist = ((float)(newX-node0x)*(float)(newX-node0x))+((float)(newY-node0y)*(float)(newY-node0y)); if( dist < oldDist ) return 0x0; return (QNODE *) malloc(sizeof(QNODE)); } void flowRecursive( node, im, x, y, depth, maxDepth , usedIDs, totalLength, targetRed, targetGreen, targetBlue , maxDistance ) QNODE * node; gdImagePtr im; Count x, y, depth, maxDepth; unsigned char * usedIDs; Count * totalLength; float targetRed, targetGreen, targetBlue , maxDistance; { float dist; node->x = x; node->y = y; depth++; usedIDs[(im->sx*y)+x] = 1; *totalLength = *totalLength + 1.0; if( depth > maxDepth ) { return; } node->nodeU = (usedIDs[((y+1)*im->sx)+x] == 0) ? testFlow( im, x, y, x , y+1, targetRed, targetGreen, targetBlue , maxDistance ) : 0x0; node->nodeD = (usedIDs[((y-1)*im->sx)+x] == 0) ? testFlow( im, x, y, x , y-1, targetRed, targetGreen, targetBlue , maxDistance ) : 0x0; node->nodeL = (usedIDs[( y *im->sx)+x-1] == 0) ? testFlow( im, x, y, x-1, y , targetRed, targetGreen, targetBlue , maxDistance ) : 0x0; node->nodeR = (usedIDs[( y *im->sx)+x+1] == 0) ? testFlow( im, x, y, x+1, y , targetRed, targetGreen, targetBlue , maxDistance ) : 0x0; if( node->nodeU != NULL ) flowRecursive( node->nodeU , im, x , y+1, depth ,

maxDepth, usedIDs, totalLength, targetRed, targetGreen, targetBlue , maxDistance ); if( node->nodeD != NULL ) flowRecursive( node->nodeD , im, x , y-1, depth , maxDepth, usedIDs, totalLength, targetRed, targetGreen, targetBlue , maxDistance ); if( node->nodeL != NULL ) flowRecursive( node->nodeL , im, x-1, y , depth , maxDepth, usedIDs, totalLength, targetRed, targetGreen, targetBlue , maxDistance ); if( node->nodeR != NULL ) flowRecursive( node->nodeR , im, x+1, y , depth , maxDepth, usedIDs, totalLength, targetRed, targetGreen, targetBlue , maxDistance ); return; }

int main( argc, argv ) int argc; char ** argv; { FILE * f_in, * f_out; Count x, y, minLength, maxDepth, depth, totalLength, maxLength; QNODE node0; gdImagePtr im_in, im_out; int colors[256], bgColor, c, useit, outline=0, i; unsigned char * usedIDs; float dist, maxDistance, targetRed, targetBlue, targetGreen;;

if( argc != 10 ) { fprintf(stderr,"usage : Q-Tree messy.gif maxDepth red green blue maxDistance maxLength clean.gif \n"); exit(-1); } if( !(f_in=fopen(argv[1],"rb")) ) { fprintf(stderr,"Failed opening %s\n",argv[1]); exit(-1); } if( !(f_out=fopen(argv[9],"wb")) ) { fprintf(stderr,"Failed opening %s\n",argv[8]); exit(-1); } maxDepth = atoi(argv[2]); targetRed = atof(argv[3]); targetGreen = atof(argv[4]); targetBlue = atof(argv[5]); maxDistance = atof(argv[6]); minLength = atoi(argv[7]); maxLength = atoi(argv[8]);

im_in = gdImageCreateFromGif(f_in); if (!im_in) { fprintf(stderr,"Error: %s is not a valid gif file.\n", argv[1]); exit(1); } im_out = gdImageCreate( im_in->sx, im_in->sy ); for( i=0; i < 255; i++ ) { colors[i] = gdImageColorAllocate( im_out, i, i, i ); } bgColor = colors[0]; gdImageFilledRectangle( im_out, 0, 0, im_out->sx-1, im_out->sy-1 , bgColor); if( !(usedIDs = (unsigned char *) malloc(sizeof(unsigned char)*im_in->sx*im_in->sy)) ) { fprintf(stderr,"Failed allocating array! \n"); exit(-1); } fprintf(stderr,"%d %d \n",minLength,maxLength); for( x=0; x < im_in->sx; x++ ) { for( y=0; y < im_in->sy; y++ ) { c = gdImageGetPixel( im_in, x, y ); dist = ((float)im_in->red[c] -targetRed) *((float)im_in->red[c] -targetRed) + ((float)im_in->green[c]-targetGreen)*((float)im_in->green[c]-targetGreen) + ((float)im_in->blue[c] -targetBlue) *((float)im_in->blue[c] -targetBlue) ; useit = 0; if( dist < maxDistance*maxDistance ) useit = 1; else useit = 0; if( useit ) { memset( &node0 , 0x0 , sizeof(QNODE) ); depth = 0; totalLength = 0; node0x = x; node0y = y; flowRecursive( &node0, im_in, x, y, depth, maxDepth , usedIDs , &totalLength , targetRed, targetGreen, targetBlue , maxDistance*maxDistance ); if( totalLength > 255 ) totalLength = 255;

if( totalLength >= maxLength ) totalLength = 0; if( totalLength numTotBridge in S 3.4, then add more bridges in DOT data. • This process needs two functions, one for CREATING a point object and another for DELETING a point object for editing the bridge location.

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Before starting editing, SELECT the appropriate layer to edit. In this case, DOT bridge data layer should be selected. If those numbers are the same, then go to the next step (Section 4.2).

4.2. DISPLAY the area of interest of each bridge by selecting bridges one by one. • The area of interest of a bridge is determined based on the bridge location from DOT data. • The SHAPE of the area of interest can be circular or rectangular. • The SIZE of the area of interest will be determined with a value the user inputs. • If the orthophoto possible bridge location is outside from the area of interest, the area of interest is shown in red color. This means that the orthophoto possible bridge location needs to be moved. Otherwise, the area of interest needs to be shown in green color. 4.3. Check the orthophoto’s possible bridge location with DOT bridge location. • SELECT the orthophoto possible bridge location layer to edit. • MOVE only the orthophoto possible bridge location displayed in red color to adjust with DOT bridge location. • In this case, by moving bridge location, the coordinate value of each bridge should be also changed together. In addition, monitor should also show those changed coordinate value. • ZOOM IN, ZOOM OUT, and PAN functions are needed for this operation. 4.4. DISPLAY results 4.5. Check if all bridges are checked. • If all areas of interest are shown in green color, then go to the next step. • Otherwise, repeat Section 4.3, 4.4. 5. CREATING REPORTS 5.1. JOIN & MERGE data tables. • SELECT both orthophoto data layer and DOT bridge data layer to edit. • After joining & merging, new data table should be created automatically. • After checking bridge location, each data table should be joined/merged together to provide overall information about each bridge. 5.2. CREATE REPORTS • The report is created based on the merged data in *.txt format. • The report contains information about each bridge, such as BRIDGE_ID, X & Y COORDINATES, and so on. 5.3. PRINT § SELECT a layer to print. § This function prints out images, texts, and spread-sheet format documents. 6. CLOSE OUT 6.1. CLOSE • Close images, text documents, and spread-sheet format documents. 12/1/2000

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APPENDIX 3 Object Oriented Structure for Class Bridge and Class Road Class Bridge The bridge is an object with point type. This class controls all information and functions for a bridge. As a data structure for this class, the binary tree with binary node type is recommended to easily preserve the order of the bridge along a route. The basic implementation of this class is based on the implementation for a binary tree node. Data Members int Bridge_ID int On/Under int Highway_NO double xPos double yPos int numBridge Function Members // Accessor int GetBridgeID (); int BridgeOn (); int getHighwayNO (); double getXPos (); double getYPos (); // Mutator void setBridgeID (int id); void setBridgeOn (int on); void setHighwayNO (int way); void setXPos (double pos); void setYPos (double pos); // Functional commands void insert (object x); void remove (object x); object find (object x); boolean isEmpty (); void makeEmpty (); void print (); void merge ();

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void split (); Class Road The road is an object with linear type. This class controls all information and functions for a road. As a data structure for this class, the binary tree with binary node type is recommended to easily preserve the order of a bridge along a route. The basic implementation of this class is based on the implementation for a binary tree node. Data Members int Arc_ID; int start_Node; int end_Node; char road_type; int road_No; double length; Function Members // Accessor int getArcID (); int getSNode (); int getENode (); char getRoadType (); int getRoadNo (); double getLength (); // Mutator void setArcID (int id); void setSNode (int node); void setENode (int node); void setRoadType (char road); void setRoadNo (int road); // Functional commands void measure (int snode, int enode); object find (object x); void print (); void insert (object x); void remove (object x); boolean isEmpty (); void makeEmpty (); void merge (); void split ();

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Use of Remotely Sensed Data for Infrastructure Management ISU Progress Report prepared for University of California, Santa Barbara November 24, 2000

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Table of Contents A – Technical Progress Summary ........................................................................ 4 B -- Business ........................................................................................................ 5 C – Payable Milestones........................................................................................ 6 1. Typology .......................................................................................................... 7 2. Remote Sensing for Access Management....................................................... 8 2.1 BACKGROUND ....................................................................................... 8 2.1.1 Introduction........................................................................................... 8 2.1.2 Literature Review.................................................................................. 9 2.1.3 Current Data Collection Methods.......................................................... 9 2.2 RESEARCH OBJECTIVES ..................................................................... 10 2.3 PROJECT DESCRIPTION ....................................................................... 10 2.4 DATA COLLECTION ................................................................................ 10 2.4.1 Data Sources...................................................................................... 10 2.4.2 Remote Sensing Data Collection Methodology ................................... 11 2.4.3 Presentation of Remotely Sensed Data.............................................. 15 2.4.4 Interoperability of Remotely Sensed Data .......................................... 15 2.5 METHODOLOGY ..................................................................................... 15 2.5.1 Methodology to Rank the Safety of Road Segments Based on Extracted Data.............................................................................................. 15 2.6 STATISTICAL ANALYSIS ........................................................................ 16 2.7 COST AND BENEFITS OF REMOTE SENSING ..................................... 16 2.8 RESULTS ................................................................................................. 16 2.9 CONCLUSIONS ....................................................................................... 16 2.9 POSSIBILITY OF AUTOMATION............................................................. 16 3. Remote Sensing for Point Feature Extraction................................................ 17 3.1 BACKGROUND........................................................................................ 17 3.1.1 Introduction......................................................................................... 17 3.1.2 Current Data Collection Methods........................................................ 17 3.2 OBJECTIVES ........................................................................................... 17 3.3 PROJECT DESCRIPTION ....................................................................... 17 3.4 DATA COLLECTION ................................................................................ 17 3.4.1 Data Sources...................................................................................... 17 3.4.2 Remote Sensing Data Collection Methodology .................................. 18 3.4.3 Presentation of Remotely Sensed Data.............................................. 18 3.4.4 Interoperability of Remotely Sensed Data .......................................... 18 3.5 METHODOLOGY ..................................................................................... 19 3.6 STATISTICAL ANALYSIS ........................................................................ 20 3.7 COST AND BENEFITS OF REMOTE SENSING ..................................... 20 3.8 RESULTS ................................................................................................. 20 3.9 CONCLUSIONS ....................................................................................... 20 3.10 POSSIBILITY FOR AUTOMATION ........................................................ 20 4. Applications of Point Feature Extraction........................................................ 21 4.1 INTRODUCTION ...................................................................................... 21 4.2 IDENTIFICATION OF PASSING ZONES ................................................. 21

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4.2.1 Methodology to Identify and Spatially Locate Passing Zones Begin and Endpoints...................................................................................................... 21 4.2.2 Associating Points to the Roadway Database .................................... 23 4.2.3 Issues ................................................................................................. 24 4.2.4 Recommendations.............................................................................. 26 4.3 USING REMOTE SENSING TO IDENTIFY FEATURES FOR LINEAR REFERENCING SYSTEMS ............................................................................ 26 5.1 BACKGROUND........................................................................................ 27 5.1.1 Introduction......................................................................................... 27 5.1.2 Current Data Collection Methods........................................................ 27 5.2 RESEARCH OBJECTIVES ...................................................................... 27 5.3 PROJECT DESCRIPTION ....................................................................... 27 5.4 DATA COLLECTION ................................................................................ 28 5.4.1 Data Sources...................................................................................... 28 5.4.2 Remote Sensing Data Collection Methodology .................................. 28 5.4.3 Presentation of Remotely Sensed Data.............................................. 29 5.4.4 Interoperability of Remotely Sensed Data .......................................... 29 5.5 METHODOLOGY ..................................................................................... 30 5.6 STATISTICAL ANALYSIS ........................................................................ 30 5.7 COST AND BENEFITS OF REMOTE SENSING ..................................... 30 5.8 RESULTS ................................................................................................. 31 5.9 CONCLUSIONS ....................................................................................... 31 5.10 POSSIBILITY OF AUTOMATION........................................................... 31 6.0 REFERENCES ............................................................................................ 32

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A – Technical Progress Summary Most of the work this quarter has centered on developing specific projects. Three topics related to the Consortium’s theme, “Infrastructure Management,” were identified and a work plan including scope and methodology developed. The projects were selected to demonstrate remote sensing applications for Departments of Transportation that could be practically applied. Meetings were held with several key people at the Iowa Department of Transportation to solicit ideas. The three projects were selected as a result of those meetings. Work has also centered on development of a user needs matrix. The matrix elements are discussed in Section 1. A more informative description will be provided when the matrix is finalized. The first project is “Remote Sensing for Access Management.” This project is investigating remote sensing applications to collection and measurement of key access management data elements. A working draft of the project is found in Section 2. Several sections have been completed, the rest are works in progress and will be filled out as the research progresses. The next project evaluates issues related to the use of remote sensing for locating and populating attribute fields for infrastructure inventory elements represented as point features. Point features include those that would normally be represented as a point feature in a geographic information system such as signs or location of drainage facilities. They also include linear features that can be located using beginning and ending points, such as passing lanes. Section 3 describes preliminary work for “Remote Sensing for Point Feature Extraction.” Several sections have been completed, the rest are works in progress and will be expanded as research continues. Section 4 briefly describes several application areas under this topic. Again, more detail will be added as the project progresses. The final project examines the use of remotely sensed data for locating and populating attribute fields of roadway intersection inventory elements. The project is especially focused on intersection inventory elements that have been identified as contributing to crash likelihood. One of the final results will be a safety analysis using the collected attributes to determine whether the ability to relatively easily add intersection attributes, such as number of turning lanes, improves crash analysis at intersections. Section 5 describes the initial work for “Identification of Intersection Attributes Using Remote Sensing for Improved Safety Analysis.”

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1. Typology The user needs matrix is attached as a separate *.pdf file. A comprehensive description of the typology is being developed and will be described at a later date.

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2. Remote Sensing for Access Management 2.1 BACKGROUND 2.1.1 Introduction The United States transportation system provides mobility to around 268 million residents and nearly 7 million businesses (BTS, 1999). Road transport is the most popular transportation mode in the United States. The country’s road network is a primary component of the nation’s transport and infrastructure, which has been created by gradual investment over many years. Such a vast and diversified network requires efficient and effective investment decisions. A management strategy which recognizes the magnitude and sensitivity of the investments that affect the capacity, condition and use of road system and that takes into account the broad range of social, financial, economic and physical implications to the community is therefore necessary. From 1990 to 1999 the estimated lane miles in United States increased by about 1.92 percent, whereas vehicle miles traveled (VMT) increased by approximately 20.84 percent (calculated using data from FHWA, 1999 and BTS, 1999). This indicates an increase in transportation demand without a corresponding increase in supply. This will induce operational degeneration of the system leading to increased traffic safety problems. Also the level of service degeneration due to increase in access points cannot be outweighed by increase in additional travel lanes. This situation emphasizes the preservation of the functional integrity and hierarchy of the existing system. Access management is an excellent transportation system management tool that will increase the overall system efficiency. Effective management of access preserves the safety and capacity of the transportation system. It also increases the functional life of existing capital investments. The Federal Highway Administration (FHWA) defines access management as "the process that provides access to land development while simultaneously preserving the flow of traffic on the surrounding road system in terms of safety, capacity, and speed. It attempts to balance the need to provide good mobility for through traffic with the requirements for reasonable access to adjacent land uses." A study conducted by Florida Department of Transportation found that the typical four-lane arterial road with good access management could handle almost 10,000 more vehicles per day than the same four-lane road with poor access management (MDOT, 1996). National Safety Council estimates the average annual cost associated with the driveway-related crashes in the state of Michigan for the period 1992 through 1994 to be more than $220 million (MDOT, 1996). The Colorado Department of Transportation estimates that $900 million is lost annual due to access-related crashes. The Minnesota Department of Transportation estimates an annual loss of $500 million due to access related crashes (MnDOT, 1999).

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2.1.2 Literature Review To date an extensive literature review has been performed. A synopsis of the literature covered will be presented in more depth in subsequent reports. Briefly the topics covered are: § Data collection procedures used for access related research § Access management techniques and methods of estimating safety and operational effects of different techniques § Aerial photography Several literature sources are listed below •

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•

• • •

Jerome Gluck, Herbert S. Levinson, and Vergil Stover, Impacts of Access Management Techniques, National Cooperative Highway Research Program, Report 420, Transportation Research Board, National Research Council, Washington, D.C., 1999. F.J.Koepke, and H.S.Levinson, Access Management Guidelines for Activity Centers, National Cooperative Highway Research Program, Report 348, Transportation Research Board, National Research Council, Washington, D.C., 1992. James A. Bonneson and Patrick T. McCoy, “Effect of Median Treatment on Urban Arterial Safety: An Accident prediction model,” Transportation Research Board, Transportation Research Record 1581, Washington D.C., 1997. McGuirk, W.W., and Staterly, G.T., Jr., “Evaluation Of Factors Influencing Driveway Accidents,” Transportation Research Board, Transportation Research Record 601 Washington D.C., 1976. Nicholas J. Garber, and Timothy E. White, “Guide Lines for Commercial Driveway Spacing on Urban and Suburban Arterial Roads,” Proceedings of The Second National Conference on Access Management, 1996. Minnesota Department of Transportation, Statistical Relationship Between Vehicle Crashes and Highway Crashes Final Report, Prepared by BRW Inc. 1998.

2.1.3 Current Data Collection Methods The identification of the appropriate roadway features is an important part of any access related project. However, collection of data on driveway spacing, driveway density, median type, median openings and other access related characteristics is time consuming and resource intensive. Therefore at this point, no state has quantitative, comprehensive access data on its entire roadway system. Data collection methods at DOT’s of related elements are currently being explored.

9

2.2 RESEARCH OBJECTIVES Literature available on access management projects indicates significant benefits in terms of traffic safety and management. A nationwide rise in congestion levels and construction costs, and the observed correlation between highway safety and level of access control have encouraged State and local transportation agencies to implement access management related projects. Data related to access elements is a very important part of any access related project. However, collection of data on driveway spacing, driveway density, median type, median openings and other access related characteristics is time consuming and resource intensive. Therefore, no state has quantitative, comprehensive access data on its entire roadway system. Without such data, systematic identification of locations that would benefit from improved access management is difficult if not impossible. As a result there is a need to explore new methods to costeffectively collect and measure key access management data. The main objective of this research was to investigate the practicality of using remote sensing to collect and measure access management data elements. If this method demonstrates a cost-effective method it will allow DOT’s to evaluate the need for access control and mitigation. The second objective was demonstrating the practical application of using the collected data for improved safety analysis. 2.3 PROJECT DESCRIPTION Research indicates a significant correlation between highway safety and level of access control. However, collection of data on driveway spacing, driveway density, median type, median openings, and other access related characteristics are time consuming and resource intensive. Therefore, no state has quantitative, comprehensive access data on its entire roadway system. Without such data, systematic identification of locations that would benefit from improved access management is difficult if not impossible. This project intends to demonstrate the practicality of collecting access related information using remote sensing and to provide recommendations on its use.

2.4 DATA COLLECTION 2.4.1 Data Sources 2.4.1.1 Satellite Images Satellite images of the study are not currently available to the study team. However, satellite images for other urban areas were cursorily evaluated to determine whether or not the access elements used in this study could be identified. The results are not directly comparable but do provide an indication of the possibility of using various types of satellite images for this type of application.

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2.4.1.2 Aerial Photography Three datasets of aerial photos with different resolutions were obtained for Story County, Iowa. The 2-foot data is ortho-rectified and panchromatic (Story county engineers office). The images are stored digitally. A digital dataset with 6-inch resolution was provided by the Iowa Department of Transportation. The 6inch data is also ortho-rectified and panchromatic. The last dataset (3-inch) also came from the Iowa DOT but originally was in hard copy format. Initially, the photos were scanned using a regular scanner. However, it was determined that the scanned quality was poor. Currently the possibility of sending the photos out for professional scanning is being investigated. After the photos are rescanned, the project team will orthorectify them for further use. 2.4.1.3 Crash Statistics Crash statistics used in the statistical analysis were extracted from the Safety Record Database maintained jointly by Iowa Department of Transportation and Center for Transportation Research and Education, Iowa State University. 2.4.2 Remote Sensing Data Collection Methodology The two-foot and 6-inch datasets were evaluated as to which access features could be identified and accurately recorded. Features collected include: • Driveway spacing • Driveway density • Driveway land use • Driveway width • Type of median • Length of median • Length of turning lanes • Length of two-way left turn lanes • Intersection type • Number of lanes Most features could not be identified with any regularity in the 2-foot images. In contrast, the majority of the features could be both identified and measured using the 6-inch images. Figures 2-1, 2-2, and 2-3 show the extraction of various access related data elements such as turning lanes, type of medians, alleys, and driveways (depending on land use) from six-inch pixel resolution orthophotos.

11

N SEVENTH

US 69

Raised Median #

ALLEY #

Left Turn Lane #

FIGURE 2-1: Extraction of Access Related Data Elements: Turn lanes, Alley and Median Type

N

TW O W AY LEFT TURN LANE #

FIGURE 2-2: Extraction of Access Related Data Elements: Two Way left Turn Lanes

12

N 13TH ST

US 69

#

#

Residential Driveways Commercial/Business Driveways (Typically all Non residential Driveways)

FIGURE 2-3: Extraction of Access Related Data Elements: Driveways / Land Use

13

Table 2.1 indicates which of the access-related features could be identified for each dataset. Table 2.1 Feature Identification Versus Resolution Access Inventory Element Driveway spacing

Driveway density

Driveway land use Driveway width

Satellite

2-foot Aerial

6-inch Aerial

3-inch Aerial

Difficult to distinguish driveways from surrounding features Difficult to distinguish driveways from surrounding features Yes

Difficult to distinguish driveways from surrounding features Difficult to distinguish driveways from surrounding features Yes

Yes

Not evaluated yet

Yes

Not evaluated yet

Yes

No

Cannot be measured accurately No

Yes

Not evaluated yet Not evaluated yet

Difficult to measure Only If there is a marked change in the geometry of the road Difficult to identify No

Yes

No

No

Yes

Yes

Yes

Yes

Type of median Length of median Presence of turning lanes

No

Length of turning lanes Length of twoway left turn lanes Intersection type Intersection spacing

No

No No

Cannot be identified

14

Yes

Yes

Yes Yes

Not evaluated yet Not evaluated yet Not evaluated yet

Not evaluated yet Not evaluated yet Not evaluated yet Not evaluated yet

2.4.3 Presentation of Remotely Sensed Data Several locations in the Ames, Iowa area were selected as study areas. Access elements were located and measured for each area using the 6-inch dataset. A more comprehensive description of the final data will be described later. 2.4.4 Interoperability of Remotely Sensed Data The access management data elements were collected to evaluate the feasibility of using remote sensing to measure and locate common access data elements and then use that data to analyze accident risk. Once access element datasets are created, either at the project level as for this research or regionwide, the datasets can be shared and used at several levels in a DOT. The most obvious use of the data is for accident analysis as for this study. Consequently the data may be of the most interest to Safety Offices. Planning and Zoning divisions may also find use for the data as well. Access data can be used for project planning, engineering studies, and maintenance.

2.5 METHODOLOGY Once, access elements were measured and located, this information was used to develop a methodology to rank road segments based on level of access control. Crash statistics of the road segments under consideration were used to relate the ranking methodology to safety. A qualitative ranking methodology was developed and compared to the quantitative analysis to determine whether the qualitative method is sufficiently accurate enough to replace quantitative assessment for statewide development. Data elements related to access such as number and type of intersections per mile, number and type of driveways per mile, driveway spacing, type of turn lanes, type of medians, and access roads are being extracted from aerial photographs. The data is being used to develop a methodology to rank road segments based on their level of access control. Digitized crash statistics of the road segments under consideration have been obtained and are being used to relate the ranking methodology to safety. The feasibility of using aerial photographs for extracting data at different resolutions will be evaluated. The possibility of qualitative ranking methodology will also be explored. If qualitative analysis (visual inspection and ranking of corridors) produces similar correlations, it may be sufficient to replace quantitative assessment. Orthophotos of 2 feet and 6 inch pixel resolution will be used for analysis. 2.5.1 Methodology to Rank the Safety of Road Segments Based on Extracted Data This task is currently in progress. Quantitative analysis is in an experimental stage. Results obtained indicate a strong relationship of access

15

related crashes to commercial driveways and left turn lane availability. Residential driveways do not show any significant relationship with access related crashes. The results obtained are based on access related data elements extracted from 15 test segments on US 69. The crash statistics used for the analysis are for a period of over three years. A comprehensive analysis is being planned. Depending on the results obtained by the analysis a best suitable existing model (accident prediction model which is based on access related data elements) would be used to develop the ranking methodology. 2.6 STATISTICAL ANALYSIS Various statistical techniques are being explored at this time. statistical methodology has not been selected at this point.

A final

2.7 COST AND BENEFITS OF REMOTE SENSING A cost/benefit study comparing traditional methods of collecting access to the method developed in this project will be conducted in the next quarter of the project.

2.8 RESULTS Research is underway and results will be presented as they become available. 2.9 CONCLUSIONS Research is underway and conclusions will be presented as they become available.

2.9 POSSIBILITY OF AUTOMATION Once research is finalized, the possibility for the method developed to be automated will be discussed.

16

3. Remote Sensing for Point Feature Extraction 3.1 BACKGROUND 3.1.1 Introduction Roadway inventory data are used by DOT’s for a variety of purposes including construction projects, traffic engineering studies, evaluation of maintenance needs, etc. A literature review is in the final stages that will describe the uses of inventory data, current data collection methods, and will identify state of the art technologies to collect inventory data. 3.1.2 Current Data Collection Methods DOT’s currently use several methods to collect inventory data including video logging vans, GPS, and distance measuring instruments (DMI). Most of these methods require actual in-field data collection, which may be both time and resource intensive. . 3.2 OBJECTIVES With the advent of high-resolution satellite imagery and advances in the field of photogrammetry, applications of remote sensing are spreading to a wide variety of fields. One of the uses of remote sensing is the use of aerial photographs to identify land-based objects. This technique is successfully employed for identification of transportation related feature extraction. This project aims to apply this technique to identify the point feature elements. The spatial accuracy and cost involved in the process are compared with the traditional methods of inventory data collection. The main objective of this research was to investigate the practicality of using remote sensing to collect and measure infrastructure elements represented as point features. It intends to provide guidance to would-be users as to recommended methods, accuracy requirements, etc. 3.3 PROJECT DESCRIPTION The use of remotely sensed images may offer a more practical and costeffective data collection method than traditional methods. The feasibility and spatial accuracy of using remotely sensed images for inventory data collection was evaluated as part of this project. This phase of the project focused on collection point inventory features including those actually represented by a point such as signs or drainage structures as well as linear features described by beginning and ending points such as location of passing lanes on rural highways. 3.4 DATA COLLECTION 3.4.1 Data Sources 3.4.1.1 Satellite Images Satellite images of the study are not currently available to the study team. However, satellite images for other urban areas were cursorily evaluated to determine whether or not the access elements used in this study could be identified. The results are not directly comparable but do provide an indication of

17

the possibility of using various types of satellite images for this type of application.

3.4.1.2 Aerial Photography Three datasets of aerial photos with different resolutions were obtained for Story County, Iowa. The 2-foot data is ortho-rectified and panchromatic. The images are stored digitally. A digital dataset with 6-inch resolution was provided by the Iowa Department of Transportation. The 6-inch data is also ortho-rectified and panchromatic. The last dataset (3-inch) also came from the Iowa DOT but originally was in hard copy format. Initially, the photos were scanned using a regular scanner. However, it was determined that the scanned quality was poor. Currently the possibility of sending the photos out for professional scanning is being investigated. After the photos are rescanned, the project team will orthorectify them for further use. 3.4.2 Remote Sensing Data Collection Methodology The two-foot and 6-inch datasets are being evaluated as to which point features could be identified and accurately recorded. More information will be provided as the project progresses. A preliminary list of features to be collected include: • Signs • Signal location • Beginning and ending of turning lanes • Drainage facilities • Railroad crossings • Bridge abutments • Changes in pavement marking configurations for passing zone identification 3.4.3 Presentation of Remotely Sensed Data Several locations in the Ames, Iowa area were selected as study areas. Point features were located and measured for each area using the 6-inch dataset. A more comprehensive description of the final data will be described later. 3.4.4 Interoperability of Remotely Sensed Data The Information Technology Division of the Iowa DOT suggested this application. The main use of the data was to populate the business elements of the DOT’s Linear Referencing System, which is in the process of being developed by outside consultants. The point feature data elements were collected to evaluate the feasibility of using remote sensing to measure and locate common infrastructure data elements that are represented as point features, including points representing the beginning and ending of linear features. Once datasets are created, either at the project level as for this research or regionwide, the datasets can be shared and used at several levels in a DOT. Planning and Zoning divisions may also find use for the data as well.

18

Point feature data can be used for project planning, engineering studies, and maintenance. The proposed research is intended to help the DOT personnel in the collection of inventory data especially point features like signs, signals, bridge abutments and linear referencing control points. It is expected that the proposed research will help these personnel to choose the required resolution of aerial photographs for identifying the features and locating then accurately. As the accuracy of identified point features from aerial photographs is validated with the actual ground location there is no need for the DOT to validate the identified features again. This helps in reducing the amount of money spent for collection of inventory data, reduces the number of personnel required for the job and eliminates the question of safety for field data collection personal as they no longer have to stay in hazardous location for collection of data. Aerial photographs are reusable and can be used for other purposes such as access management, pavement management etc. Once accurate location data is collected it can be used for asset management and inventory management purposes. Further research in feature extraction and development of automated extraction tools will further improve the accuracy. 3.5 METHODOLOGY Features that can be identified and their spatial location on the ground collected using global positioning systems (GPS). The project will focus on evaluating which features can be identified and located for different resolutions of remotely sensed images. The following steps describe the expected approach of the project. •

The study area should have a wide variety of infrastructure elements, so that it can be evaluated as to which elements can be extracted accurately from the aerial photographs. • Two areas in Ames selected after checking for good number of infrastructure elements. • Aerial photographs at different resolutions are obtained for these areas, scanned, and analyze in GIS environment. • The Latitude and Longitude of individual elements are calculated from the images. • The actual ground co-ordinates (ground truth) are obtained using a differentially correctable GPS unit. • The error in the co-ordinates of each element from the photographs and the actual ground co-ordinates is calculated and compared using Root mean square test. The cost of collecting inventory elements by this method is also evaluated by benefit-cost analysis.

19

3.6 STATISTICAL ANALYSIS Various statistical techniques are being explored at this time. statistical methodology has not been selected at this point.

A final

3.7 COST AND BENEFITS OF REMOTE SENSING The cost of the data collection using remote sensing will be estimated and compared to the traditional data collection methods identified in section 3.1.2. 3.8 RESULTS Research is underway and results will be presented as they become available. It is expected that the error in the measured co-ordinates from the photographs will be minimum compared to the actual ground truth. The R2 value from the root mean square test will provide the basis for this result. It is also expected that the benefits of using aerial photographs will be more than the cost of collecting the data i.e. the B/C ratio is expected to be greater than 1.0. The results will not only reveal the possibility of using aerial photographs for collecting inventory data but also look into technical details for the required resolution of the photographs. 3.9 CONCLUSIONS Research is underway and conclusions will be presented as they become available.

3.10 POSSIBILITY FOR AUTOMATION Once research is finalized, the possibility for the method developed to be automated will be discussed.

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4. Applications of Point Feature Extraction 4.1 INTRODUCTION This chapter discusses several specific applications of the use of remote sensing to identify point feature inventory elements as described in Chapter 3. 4.2 IDENTIFICATION OF PASSING ZONES Passing zones are identified by pavement marking configurations that provide guidance to drivers as to whether the geometric layout of the roadway allows sufficient sight distance for a following vehicle to pass a slower moving one. They are primarily located on 2-lane roadways. An inventory of passing zones is useful in safety analysis and for design projects. It may also be useful in roadway maintenance and rehabilitation and in scheduling crews to repaint pavement markings. 4.2.1 Methodology to Identify and Spatially Locate Passing Zones Begin and Endpoints The simplest method to represent changes in pavement marking configuration is to represent them using point features that delineate where changes occur such as the end of a set of double yellow lines and the beginning of solid yellow on one side and broken yellow on the other. If linear infrastructure features, such location of passing and no passing zones, can be related to a corresponding geographic street database, they can initially be represented as a set of points that marking the beginning and ending location of the feature. Point features can then linked to a corresponding street database. The beginning and ending points of passing lanes were manually identified using aerial photographs of the study area. The 2-foot images described in Section 3.3.1.2 were first evaluated. Pavement marking could not be clearly identified at this resolution. Although pavement markings could not be clearly detected manually, at some point technology may allow analysis on another level. The 6-inch dataset, also described in Section 3.3.1.2, was evaluated and did provide the level of detail necessary to manually identify pavement markings. Passing zones could also be detected using the 3-inch dataset, also described in Section 3.3.1.2. However since the 3-inch dataset has not yet been orthorectified, it could not be used to relate features to a geographic street network. Consequently, the 6-inch dataset was used for analysis. Once it was determined that the 2-foot dataset could not be used, the 6inch dataset was examined to isolate roadways that had passing zones that could be used for analysis. A majority of the images covered roadways in urban areas, which typically do not contain passing zones. As a result only a few stretches of roadway could actually be used in the analysis. Images were analyzed using ArcView’s Image Analyst. A point feature layer was created in ArcView that overlaid the images. For each 2-lane rural highway, any change in pavement marking configuration was recorded as a point as shown in Figure 4.1.

21

With the base point established, identification of passing/no passing zones began. The identification was done manually, with the beginnings and endings of passing zones marked with a point. For each point, a corresponding record was added to the accompanying attribute table to identify the pavement marking changes. Desired data, such as if the point added represented the start or end of a passing zone, was added by the user as noted in Figure 4.2. For this project, passing zones on roads running horizontally were considered to start on the left of the view and end on the right.

• Point feature to delineate changes in pavement marking  Street database centerline

Figure 4.1: Identification of the Changes in Pavement Marking Configurations for Passing Zones

22

Figure 4.2: Start And End Points In Passing Zone With Related Attribute Table

4.2.2 Associating Points to the Roadway Database Remote sensing is typically used to spatially locate objects or to derive information about spatial objects such as size or color. As such, information from remote sensing is usually stored and accessed through some type of spatial platform such as a geographic information system (GIS). The location of passing zones is only useful when related to a geographic street database. The platform used for this study was ArcView GIS 3.2. Changes in pavement marking configurations were first located as points after using aerial photographs as described in Section 4.2.1. Once the beginning and ending points were established, dynamic segmentation was used to create a linear referencing method along a corresponding roadway database that inventoried the location of passing/no passing zones. This process is visually described in Figure 4.3.

23

Figure 4.3: Using Point Features to Linearly Reference Passing Zones

4.2.3 Issues In the course of developing this process, several issues were encountered that may also be encountered in future applications. These issues should be noted: •

The lack of color photographs limited the process of examination and analysis. Without such data, comparisons could not be drawn between the use of color and black and white photography in identification of pavement markings and passing zones.

•

Shadows made the identification of pavement markings difficult in some areas. In most cases, this did not totally prevent identification of pavement markings, it simply made the process more difficult.

•

The brightness and contrast of an image also made pavement markings more difficult to distinguish. If using a version of ArcView with the Image Analysis extension, these problems can be corrected by readjusting the brightness and contrast.

24

•

Faded pavement markings also posed problems during image analysis. Faded markings tended to “blend” into the pavement, a problem particularly compounded by the nature of the images being black and white.

•

Dust and dirt on the road surface also presented problems as they obliterated the view of pavement markings in some areas. The extent to which this problem occurred during analysis is unknown, but it is an issue that should be noted.

•

Rutting can lead to confusion as to where pavement markings are located, as illustrated in the image below in Figure 4.4.

Figure 4.4: Rutting Along Roadway That Obscures Pavement Markings

Many of these issues might be resolved through the use of color photography. Several color images of roadway surfaces not related to the study area were examined and it was noted that features such as pavement markings stand out more prominently. The use of color images could possibly compensate for the shortcomings of black and white images listed above.

25

4.2.4 Recommendations

Current: • Image resolutions of 6 inches or higher provide the amounts of detail necessary to identify pavement markings and subsequently passing zones. •

A knowledge of the location of two lane roads would be helpful as potential passing zones could quickly be identified and examined rather than panning through the image(s) attempting to find all the roads that may have passing zones.

•

The usefulness of color photographs needs to be determined so that their applicability in identifying passing zones can be established. This analysis will allow a comparison to be made between the levels of detail provided by color and black and white images. Such a comparison will help to determine which type of image will be more applicable in identifying passing zones.

Future: • Development of an automated method for enhancing pavement markings in an image is needed. With such enhancement, manual identification of markings will become less difficult and the accuracy in determining where passing zones begin and end will be greatly improved. 4.3 USING REMOTE SENSING TO IDENTIFY FEATURES FOR LINEAR REFERENCING SYSTEMS The use of remote sensing to establish features that can be used in a linear referencing system is also underway. Again, most features that can be related in a linear manner to a geographic street database can first be located using point features as described in Section 4.2. The use of image datasets for spatially locating and collecting information about linear referencing items such as: • Location of control points • Calculation of control segment lengths • Identification of non-datum intersections • Population of business data are being explored and will be described at a later date.

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5. Identification of Intersection Attributes Using Remote Sensing for Improved Safety Analysis 5.1 BACKGROUND 5.1.1 Introduction Traffic accidents in the United States create a significant burden to society in the form of cost, injury, and loss of life. Annually, the National Highway Traffic Safety Association (NHSTA) estimates $150 billion in losses due to highway crashes. Iowa makes up 1% of this total. Collisions at roadway intersection are one of the most common crash types with nearly 50% of crashes occurring at or near intersections. To reduce crashes, safety analysis is used to determine roadway features contributing to crash likelihood so that safety funds can be targeted for mitigation. Effective analysis of intersection crashes, depends on the ability to properly model and analyze roadway intersections. Use of intersection feature data such as location of stopbars, turning lanes, etc. has not been exploited extensively since their collection can be time consuming, expensive, and labor intensive. In fact, in the absence of advanced technologies, such as GPS, it is extremely difficult to spatially locate intersection features at all. GPS does allow collection of highly accurate position data but requires extensive infield work for collection. 5.1.2 Current Data Collection Methods The existing methods used to inventory intersection characteristics are currently under evaluation. 5.2 RESEARCH OBJECTIVES The main objective of this research was to investigate the practicality of using remote sensing to collect and measure key intersection inventory elements. The second objective was demonstrating the practicality of using the collected data for improved intersection safety analysis. The use of remote sensing will be explored to determine if it offers a more practical way to collect and use intersection infrastructure data. 5.3 PROJECT DESCRIPTION This project examines the use of remotely sensed data for extraction of intersection features, especially those suspected of contributing to crash likelihood. The project estimates the accuracy and cost effectiveness of remote sensing compared to existing methods. The collected data will be tested in intersection safety analysis to evaluate the effectiveness of collecting and using intersection feature data for crash analysis.

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5.4 DATA COLLECTION 5.4.1 Data Sources 5.4.1.1 Satellite Images The study area covers the City of Ames in Story County, Iowa. The research team chose Ames since the Iowa Department of Transportation had aerial photos of the study area, which were relatively current and were available at no cost. Acquisition of satellite images could not be covered by the research budget for this year of the study. Once existing aerial photo datasets are analyzed and the accuracy and possibility of extracting individual intersection elements established, satellite images of different locations would be used to determine whether those features could be identified on images with lower resolution. Although not directly comparable this method will allow some conclusion to be made as to whether satellite based sensing may be used and which features can be identified.

5.4.1.2 Aerial Photography Three datasets of aerial photos with different resolutions were obtained for the City of Ames in Story County, Iowa. The 2-foot data is ortho-rectified, panchromatic with the images stored in a digital format. A dataset with 6-inch resolution was provided by the Iowa Department of Transportation. The 6-inch data is also ortho-rectified, panchromatic, and stored digitally. The last dataset (3-inch) also came from the Iowa DOT but originally was in hard copy format. Initially, the photos were scanned using a regular scanner. However, it was determined that the scanned quality was poor. Currently the possibility of sending the photos out for professional scanning is being investigated. After the photos are rescanned, the project team will orthorectify them for further use. 5.4.1.3 Crash Statistics Crash statistics used in the statistical analysis were extracted from the Safety Division of the Iowa DOT for the years 1990 thru 1998. The crash statistics for 1999 should be available by the end of the next quarter of research work. 5.4.2 Remote Sensing Data Collection Methodology The research team is in the evaluation process to determine which intersection features can be identified and whether they can be accurately located spatially and whether attributes such as length or width can be measured. Both the 2-foot and 6-inch datasets are being evaluated. If the 3-inch dataset becomes usable, it will also be evaluated. The final list of features that will be collected is to be determined after the literature review is completed. A preliminary list is shown in Table 5.1

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Table 5.1: Intersection Features Collected Using Various Remote Sensing Datasets Intersection Point Feature Number of lanes Presence of turning lanes Length of turning lanes Type of traffic control Intersection sight distance Dimensions of pedestrian facilities Location of stopbars Presence of pedestrian islands Type and presence of medians

Satellite

2-foot Aerial

6-inch Aerial

3-inch Aerial

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

Not available

5.4.3 Presentation of Remotely Sensed Data Several locations in the Ames, Iowa area were selected as study areas. Point features were located and measured for each area using the 6-inch dataset. A more comprehensive description of the final data will be described later. 5.4.4 Interoperability of Remotely Sensed Data The intersection inventory elements were collected to evaluate the feasibility of using remote sensing to measure and locate common intersection data elements and then use that data to analyze accident risk. Once intersection infrastructure element datasets are created, either at the project level as for this research or regionwide, the datasets can be shared and used at several levels in a DOT. The most obvious use of the data is for accident analysis as for this study. Consequently the data may be of the most interest to Safety Offices within a Department of Transportation. Information about intersection features is also valuable in traffic engineering studies. Most traffic engineering studies utilize simulation or traffic analysis software to establish current operational parameters like level of service (LOS) or volume to capacity ratio (v/c). Detailed

29

information about an intersection’s physical characteristic such as number of lanes, presence of turning lanes, etc. is usually required as input variables. Often this data must be collected in the field each time a new study is conducted. Manual data collection of this type is time consuming and requires project personnel to be present near or within traveled roadways. Planning and Zoning divisions may also find use for the data as well. An inventory of intersection infrastructure data can be used for project planning, maintenance, and linear referencing systems.

5.5 METHODOLOGY A literature review is underway to identify all intersection features, which have been correlated to crash risk at intersections. Once identified, those features as a minimum will be included as inventory elements. At least two levels of resolution are available to the study team for analysis. A set of aerial photography images at 2-foot and 6-inch resolution are available and being used as described in Section 5.3.1. A set of images with 3-inch aerial photos for a limited section of the study area are available and may be used if they can be scanned and orthorectified. Spatial accuracy of the extracted element attributes such as spatial position, length, etc. will be evaluated and then used independent variables in a statistical analysis to determine the strength of relationship between observable intersection features and crash likelihood. Once this work is completed, if crash reduction factors an be obtained from secondary sources, a cost/benefit analysis will be performed to determine if the proposed use of remote sensing in feature identification is an improvement over existing methods and to determine its usefulness in improving crash analysis.

5.6 STATISTICAL ANALYSIS The methodology for statistical analysis is being developed and will be reported when available. It is expected that all the work leading up the statistical analysis will be completed by the May 14, 2001 deadline for the first year’s efforts and that the statistical analysis will be completed in the first quarter of the second year.

5.7 COST AND BENEFITS OF REMOTE SENSING A cost/benefit study comparing traditional methods of collecting access to the method developed in this project will be conducted in the next quarter of the project.

30

5.8 RESULTS Research is underway and results will be presented as they become available.

5.9 CONCLUSIONS Research is underway and conclusions will be presented as they become available. 5.10 POSSIBILITY OF AUTOMATION Once research is finalized, the possibility for the method developed to be automated will be discussed.

31

6.0 REFERENCES BTS, 1999. Bureau of Transportation Statistics. U.S. Department of Transportation, Transportation Statistics Annual Report 1999. Chapter 1: System Extent and Condition. FHWA, 1999. Federal Highway Administration, U.S. Department of Transportation. Highway Statistics 1999, Section 5: Roadway Extent, Characteristics, and Performance. BTS, 1999. Bureau of Transportation Statistics, U.S. Department of Transportation. National Transportation Statistics 99. Chapter 1: The Transportation System. MDOT, 1996. Michigan Department of Transportation. Improving Driveway and access management in Michigan. A brochure developed by Mr. David Geiger of Michigan DOT, 1996. Mn/DOT, 1999. Office of Access Management, Minnesota Department of Transportation, Highway Access Management Policy Study, 1999.

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NCRST INFRASTRUCTURE Quarterly Report #2 University of Florida November 29, 2000

University of Florida (UF) activities during this quarter have focused on the applications of Airborne Laser Swath Mapping (ALSM) and Airborne Digital Photography (ADP) to a number of transportation infrastructure problems in Tampa, Hillsborough County, Florida. Meetings with County and State DOT personnel led to the development of the three projects outlined below.

Project #1:

Use of ALSM Data for Highway Planning Surveys - The highway construction process usually proceeds in 4 steps: planning, layout, final design, and as-built analysis. Each successive step requires topographic data that are more accurate and current. Repaving and bridge design projects also require high resolution digital elevation models (DEMS). The data to support these projects is presently achieved by a combination of photogrammetric and field surveying techniques. In this project, we investigate, in collaboration with Florida DOT engineers, the applicability of Airborne Laser Swath Mapping Data (ALSM), and the combination of ALSM data with digital photography, for supporting these types of transportation projects. The approach oulined would be for UF to conduct data acquisition flights of Hillsboro County using the University of Florida's ALSM system with digital camera. Process all navigation and image data in the SURFER software package. Conduct accuracy analysis against ground surveys. Present results to Florida DOT personnel. Document results

Project #2:

Use of Airborne Laser Swath Mapping Data to Generate 1-foot Contour Maps - The Stormwater and Highway Sections of the Hillsboro County Government (Tampa), and the Southwest Florida Water Management District (SWFWMD), are presently using 27 year old contour maps for hydrologic

planning purposes. These maps have a countour interval of 1 foot. The county government is embarking on a large project to remap the county using conventional analytical photogrammetric techniques. SWFWMD engineers do not have confidence in the accuracy of DEMS produced by digital photogrammetric techiques. This project is designed to evaluate the accuracy of elevation data, and contours, produced using the University of Florida's Airborne Laser Swath Mapping System (ALSM). The approach oulined would be for UF to conduct data acquisition flights of Hillsboro County using the University of Florida's ALSM system with digital camera. Process all navigationa nd image data in the SURFER and IDL software packages. Present results to Hillsboro County Surveyor and compare products to those expectected using a conventional photogrammetric approach. Document results. UF has already flown a small section of Tampa and a sample product is shown below.

Project #3:

Use of Airborne Laser Swath Mapping data in Digital Airport Layout Plans Digital Airport Layout Plans (ALP) are CADD-based tools which support the rapid analysis of the 3-dimensional airspace at major airports. Implementation of an ALP requires a robust digital elevation model (DEM) which must include all obstructions to the FAA Part 77 airspace surfaces, roof tops of buildings, the airport terrain at contour intervals as fine as 2 feet, the delineation of all airport pavement areas, and the runway end points. At present, most data to support these implementations has been acquired using photogrammetric techniques. In this project, we investigate the utility of ALSM data, combined with digital imagery, to support the necessary data sets for a digital ALP at the Plant City, Florida, airport. The approach outlined would be for UF to Conduct data acquisition flights of Plant City airport using the University of Florida's ALSM system with digital camera. Process all navigation and image data in the SURFER and IDL software packages, and conduct mapping and analysis in AutoCAD. Present results to aviation authority and compare products to those expected using a conventional photogrammetric approach. Document results.

Sample Results from Project #2 Hillsborough County, is currently working jointly with the South West Florida Water Management District (SWFWMD) on the first year of a 4 to 5 year project to create a digital elevation model (DEM) of the entire County, approximately one thousand square miles (about 225 square kilometers). The DEM will be created by conventional analytical photogrammetric techniques, and will be used with separate photographic coverage to produce one foot (30 centimeter) contour maps. Representatives of UF and Hillsborough are drafting a Memorandum of Understanding to jointly compare the time, cost, and accuracy of the photogrammetric and

Northing (ft.)

ALSM techniques, to determine the relative merits of the two approaches.

1,314,000

1,312,000 504,000

506,000

508,000

510,000

512,000

514,000

516,000

Easting (ft.)

Figure 1. This shaded relief map produced from the ALSM observations shows a portion of the downtown area of Tampa, Hillsborough County, Florida.

Figure 2. Color Digital Image of a Crosstown Expressway high rise bridges and nearby drawbridge which is at a lower elevation. ALSM data can be used to accurately measure and study the drainage pattern.

Future Activities Plans to collect ALSM and ADP data for Projects 1 and 3 were delayed due to perform aircraft maintenance. Time table is currently being rescheduled to coincidw with the NOAA hyperspectral data collection in the same area which should make the data fusion of ALSM and hyperspectral data easier.