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": {
                "class": "cars",
                "area": 7.6,
                "count": 1,
                "orientation": -28.5,
                "aspectRatio": 0.22,
                "confidence": 0.95,
                "reflectance": {
                    "blue": 0.0123,
                    "near-ir2": 0.0234,
                    "near-ir1": 0.01,
                    "yellow": 0.02,
                    "green": 0.09,
                    "red-edge": 0.12,
                    "coastal": 0.001,
                    "red": 0.123
                }
            },
            "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; always present.
  • area – area of detected object in \(m^2\); always present.
  • count – number of objects represented by the Feature; always present.
  • 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; may be unavailable.
  • aspectRatio – ratio between longer side and shorter side of the minimum-area enclosing oriented rectangle; may be unavailable.
  • confidence – a number between 0 and 1; may be unavailable.
  • reflectance – per-band reflectance of the object. Value is JSON where keys are band names and values are reflectances; may be unavailable.

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
}

Statistics for Band Pixel Intensities

Per band statistics computed from band pixel intensities (currently, only arithmetic mean). Note that mean might not be available if the analyzed area does not contain any valid data (i.e. analyzedArea is equal to zero).

Example:

{
    "heatmap": {
        "analyzedArea": 932515.2,
        "meanHeat": 0.013828165152889
    }
}
  • analyzedArea - Area of analyzed region in \(m^2\).
  • meanRadiance - Mean flux power per unit of solid angle (see Radiance and Reflectance) computed inside the analyzed area. Only available for datasets with radiance coefficients.
  • meanHeat - Arithmetic mean of structural dissimilarity (in range \([0, 1]\)) for change detection or normalized difference index, e.g. NDVI, (in range \([-1,1]\)) computed inside the analyzed area. Available for all heatmap map types except for ``nl-change``.
  • meanAbsChange - Arithmetic mean of absolute values of relative change (in range \([0, 1]\)). Available for ``nl-change``.

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).

Analysis Metadata

Analysis Metadata JSON has the following properties:

  • sScore – s-score is a value between 0 and infinity that indicates suitability of imagery for analysis by the particular algorithm; s-score lower than 1 signifies imagery of superior quality. Values higher than 3.5 indicate imagery of inferior quality and less representative results.
  • algoVersion – version of the algorithm that produced this analysis. Note that each algorithm (potentially on each dataset) is versioned independently.
  • binarizationMethod – method determines process of converting probabilities into predictions. Possible choices are [“bepThreshold”, “argmax”].
  • bepThreshold – break even point threshold. Must be in <0, 1> when binarizationMethod is set to “bepThreshold”, not specified otherwise.

Example:

{
    "sScore": 1.22,
    "algoVersion": "148",
    "bepThreshold": 0.55,
    "binarizationMethod": "bepThreshold"

}

Segmentation SKI

Segmentation SKI files have a separate band for each detection class. The bands are named after the classes, for example band with cloud segmentation is called clouds. Bands are uint8 where “1” means that the particular pixel belongs to the class and “0” means the opposite.

Apart from geo-referencing metadata the SKI may also contain analysis metedata.