Output GeoJSON of detection algorithms

GeoJSON with a FeatureCollection where each Feature represents an individual detection. All features have geometry of type Polygon and properties which are specific to algorithm which this is result of.

geometry of each region is its minimum-area bounding rectangle.

Note

Minimum-area bounding rectangles can have any orientation, i.e. they are not necessarily aligned with parallels and meridians or the feature’s orientation property.

{
    "type": "FeatureCollection",
    "features": [
        {
            "type": "Feature",
            "properties": {},
            "geometry": {
                "type": "Polygon",
                "coordinates": [
                    [
                        [153.12104687732085, -27.388643101636596],
                        [153.12137530696452, -27.389043532193686],
                        [153.12076821186633, -27.389436088316238],
                        [153.12043978372787, -27.389035658966332],
                        [153.12104687732085, -27.388643101636596]
                    ]
                ]
            }
        }
    ]
}

Object detection feature properties

  • class - determines object classification
  • area - area of detected object in \(m^2\)
  • count - number of objects represented by the Feature
  • orientation - angle between x-axis of an image and axis of a detected object given in degrees (counter-clockwise); present only if count is 1

Output JSON of segmentation algorithms

Output JSON contains square meters of area covered by each segmentation class.

Example:

{
    "water": 1161.74,
    "roads": 0.0,
    "urban": 0.0,
    "nonurban": 2857.35,
    "clouds": 0.0
}

Attention Regions

Attention regions are areas within the analyzed extent where a potentially interesting change has happened. Attention regions GeoJSON has following properties:

  • meanChange - Average value of the change heatmap inside the attention region.
  • compactness - a float number between 0 and 1 (inclusive). It measures compactness, where 1 means the attention region has fully convex shape (e.g. circle).
  • linearity - a float number between 0 and 1 (inclusive). It measurers region linearity, where 1 means the attention region has very linear (eccentric) shape (e.g. narrow straight road).