Infrastructure Change Detection SPOT

Infrastructure Change Detection is based on SPOT (1.5m) or Pléiades (0.5m) imagery. This module detects changes in man-made infrastructures such as roads, buildings and houses,
earthworks, etc. with a very high accuracy. This can be used for defense monitoring, ISR, urban monitoring, property tax change detection, unpermitted change detection and other use cases.

Technical Description

HyperVerge’s deep learning based change detection algorithms operate on high-resolution Pleiades or low-resolution SPOT imagery and highlight regions where man-made infrastructure changes have occurred with a high accuracy.

Use Cases

Federal: Monitor sensitive areas of interest to detect any changes that occur in the area such as construction of roads, buildings, artificial islands propping, etc. to ensure that the
petabytes of data can be transformed to trusted, tactically relevant and actionable insights.
Local Governments: Understand where unpermitted changes are happening to take appropriate action for property tax, permit violations thereby increasing compliance, revenue and database accuracy. The same modules can also be used for urban planning.

GIS/Mapping Companies: Keep a tab on which areas are changing and which regions need an update in the maps without having to do any manual work. Let change detection do the rest for you.

Supported workflows

Data platform:

• Catalog -> SPOT 6/7 Display -> Order and get the asset from storage of UP42
• In Projects workflows, Processing from Storage -> DIMAP GeoTIFF conversion -> Raster CRS Conversion (CRS: EPSG:3857) -> Raster Tiling (tile size: 512, match_image_extents = True) -> Infrastructure Change Detection SPOT

Data blocks:

• SPOT 6/7 (Streaming) -> Raster Tiling (tile size: 512, match_image_extents = True) -> Infrastructure Change Detection SPOT

Set the following parameters to run it successfully:

• Data blocks: Limit set to 2 for Data blocks and use contains filter instead of intersects or bbox
• Data platform: Set two asset IDs for Processing from Storage
• Set CRS to EPSG:3857 in Raster CRS Conversion
• Set tile size in Raster Tiling to width = 512 and height = 512
• Set augmentation factor = 2
• ‘match_image_extents’ in Raster Tiling set to True

Note: This block fails if less or more than two images are given.

Manual QA Process

Please note that this algorithm was optimized for a high recall score. Therefore, a manual QA process is required to filter out irrelevant predictions.