Problem Definition

Our focus in this competition is on processing geospatial LiDAR point clouds for extracting geospatial objects and finding the footprint of detected objects (i.e., building perimeter). A LiDAR point cloud is a dense array of points that represents for each point its height. Based on the patterns of the points it is possible to detect buildings (and other geospatial objects). The goal is to classify the objects and find their perimeter as accurately as possible.

The LiDAR data that we will use in the competition is data that is provided by the USGS as part of the 3D Elevation Program (3DEP): https://www.usgs.gov/3d-elevation-program. The files are provided as LAZ files which are compressed LAS files, see https://en.wikipedia.org/wiki/LAS_file_format. These files can be viewed using QGIS ( https://www.qgis.org/) and other standard tools.

The goal of the competition is to process the given LiDAR point cloud and return a set of polygons for detected buildings. The computed polygons should represent the perimeters of detected buildings as accurately as possible. This means the following:
  • Each building should be detected and a polygon should be computed for it.
  • The polygon of a building should be as close as possible to the perimeter of the building.
  • A single building should not be covered by several polygons.
  • Polygons should not cover areas that are not buildings.

The problem definition is as follows:

Input:

  • A .laz file representing a LiDAR point cloud for a given area in the USA. Note that different files may use different coordination systems. You may need to apply transformation to the desired coordination system, e.g., EPSG 3857.
  • The name of the output file.

Output:

  • A file containing the perimeters of detected buildings.
  • The results will be written to the output file as a list of polygons in a GeoJSON format.
  • All the results should be presented using an EPSG 3857 coordination system (WGS 84 / Web Mercator).

Objective:
Discover buildings based on the elevation data in the LiDAR point cloud and accurately compute their footprint.

The following is an example of an area in Arizona and polygons that are extracted for it.

The LAZ files will be provided as an input.

LiDAR Point cloud
Figure 1: LiDAR point cloud of a given area in Arizona

The results will be compared with manually extracted features based on areal images like the following image.

Areal image
Figure 2: areal image of the processed area

This is an example of polygons extracted for buildings in the area:

Extracted buildings
Figure 3: extracted buildings in the area.

The result file will be a GeoJSON file with polygons for buildings. For example, see the file output.json { "type": "FeatureCollection", "crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:EPSG::3857" } }, "features": [ { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756318.157816987, 3976734.1608545613], [-12756395.287682474, 3976732.743770412], [-12756395.490123069, 3976728.8973991494], [-12756400.146256678, 3976728.6949585574], [-12756400.348697271, 3976699.5435132035], [-12756390.429108271, 3976700.1508349837], [-12756390.226667678, 3976723.229062552], [-12756316.538292255, 3976724.4437061083], [-12756318.157816987, 3976734.1608545613]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756389.821786497, 3976690.2312459415], [-12756399.538934903, 3976690.2312459415], [-12756398.729172537, 3976642.657706653], [-12756389.214464718, 3976643.0625878386], [-12756389.821786497, 3976690.2312459415]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756378.687553944, 3976637.394251241], [-12756398.729172537, 3976637.191810648], [-12756399.336494312, 3976630.9161522742], [-12756379.497316314, 3976630.7137116785], [-12756378.687553944, 3976637.394251241]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756349.940989904, 3976659.4602758447], [-12756350.143430497, 3976656.221226364], [-12756356.419088842, 3976656.221226364], [-12756356.216648253, 3976642.2528254665], [-12756333.543301966, 3976643.0625878386], [-12756333.340861375, 3976655.006582806], [-12756336.377470253, 3976654.8041422158], [-12756336.782351438, 3976659.6627164395], [-12756349.940989904, 3976659.4602758447]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756351.762955232, 3976693.26785483], [-12756357.02641062, 3976693.470295424], [-12756357.02641062, 3976688.004399421], [-12756351.560514642, 3976688.004399421], [-12756351.762955232, 3976693.26785483]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756343.867772153, 3976687.599518235], [-12756363.504509557, 3976687.599518235], [-12756362.897187782, 3976672.214033189], [-12756343.66533156, 3976672.4164737826], [-12756343.867772153, 3976687.599518235]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756285.041441996, 3976735.459569291], [-12756285.236012734, 3976725.341890783], [-12756263.249519156, 3976724.174466342], [-12756263.249519156, 3976735.65414003], [-12756285.041441996, 3976735.459569291]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756308.973642971, 3976733.1247204035], [-12756313.837911459, 3976733.1247204035], [-12756313.448769985, 3976725.731032263], [-12756308.973642971, 3976726.1201737467], [-12756308.973642971, 3976733.1247204035]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756248.267572204, 3976736.6269937316], [-12756248.267572204, 3976723.2016126374], [-12756238.149893744, 3976723.5907541206], [-12756238.149893744, 3976736.432422993], [-12756248.267572204, 3976736.6269937316]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756253.910123656, 3976665.0249612327], [-12756253.131840697, 3976633.504501277], [-12756235.42590339, 3976634.2827842366], [-12756236.787898568, 3976665.2195319715], [-12756240.873884099, 3976665.8032441954], [-12756240.873884099, 3976669.110946786], [-12756243.208732976, 3976669.500088265], [-12756242.430450017, 3976672.0295078903], [-12756248.073001465, 3976671.83493715], [-12756248.462142944, 3976665.9978149356], [-12756253.910123656, 3976665.0249612327]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756253.831281563, 3976711.3464802764], [-12756253.98521541, 3976700.6480777357], [-12756237.822161281, 3976700.4171769633], [-12756237.822161281, 3976711.19254643], [-12756253.831281563, 3976711.3464802764]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756229.824447991, 3976736.63745899], [-12756229.899093524, 3976732.606600156], [-12756233.556724673, 3976732.084081419], [-12756233.258142538, 3976712.8255336513], [-12756219.821946481, 3976712.8255336513], [-12756219.821946481, 3976723.425199477], [-12756170.630539397, 3976724.0970092923], [-12756170.182666196, 3976704.0920061823], [-12756156.895761205, 3976704.2412972534], [-12756157.41827994, 3976732.8305367683], [-12756160.628037889, 3976733.800928711], [-12756161.07591109, 3976737.831787545], [-12756229.824447991, 3976736.63745899]]] } }, { "type": "Feature", "properties": { }, "geometry": { "type": "Polygon", "coordinates": [ [[-12756209.171695776, 3976678.829323239], [-12756209.09273263, 3976653.431096209], [-12756230.73993739, 3976652.386058735], [-12756230.81458302, 3976640.741354937], [-12756199.687395485, 3976640.59206387], [-12756199.314167818, 3976635.8893952295], [-12756188.490565438, 3976636.113331829], [-12756188.266628835, 3976639.8456085306], [-12756184.310415551, 3976640.144190666], [-12756183.86254235, 3976641.1145826075], [-12756178.935937129, 3976642.3089111503], [-12756160.573135851, 3976641.71174688], [-12756160.42384478, 3976645.07079591], [-12756156.019758295, 3976645.7426057146], [-12756156.467631377, 3976686.1258400646], [-12756158.483060785, 3976686.2751311325], [-12756158.483060785, 3976691.276381907], [-12756169.903827434, 3976691.127090842], [-12756169.455954233, 3976656.1183354016], [-12756196.77621955, 3976655.7451077327], [-12756197.59732042, 3976679.407742007], [-12756209.171695776, 3976678.829323239]]] } } ] }