- Converts 16 bit imagery from Sentinel-2 into 8 bit imagery.
- This block provides a water-related geohazards prediction processing workflow with Sentinel-2 Geotiff format scenes. This processing block is developed and launched for providing the users with a convenient way to query for the water-related geohazards (such as flooding, exposure to the water body, etc.) for their interested areas. The users can use this Platoi's processing block to query any place on the earth at any time for the predicted water-related geohazards. The machine learning model for this block has been recently optimised in October 2020
- Orbital Insight’s aircraft detection algorithm uses wide area object detection in Pleiades imagery to classify multiple types of commercial passenger and military aircraft at scale. Geospatial intelligence analysts can now conduct pattern of life analyses and activity based intelligence at global airfields of interest to conduct deeper analyses.
- Finds bounding boxes around clearly separated buildings.
- Detect apartments, houses, industrial buildings and sheds in satellite images.
- Detects buildings for each pixel in the input image it returns the probability map of it to be a part of a building.
- Burnt Area Extraction by temporal analysis for forest fires.
- Orbital Insight’s car detection algorithm uses wide area object detection in Pleiades imagery to accurately identify and quantify cars. This saves analysts significant amounts of time in conducting pattern of life analyses and activity based intelligence.
- Infrastructure Change Detection, based on SPOT or Pléiades imagery, detects changes in man-made infrastructures such as roads, buildings and houses, earthworks, etc. It is intended for urban development monitoring.
- Detects changes between two images and returns a change map
- Provides cloud masks for Pléiades and SPOT imagery.
- Performs automatic detection and correction of sub-pixel misalignments between remote sensing datasets.
- Detection and matching of features from satellite images.
- Counts the number of vector objects.
- Converts DIMAP to GeoTIFF format.
- Calculates Enhanced Vegetation Index(EVI) from satellite images.
- Shows farmers their intra-field variability in fertilizer needs
- Creates a binary field mask for SPOT blocks.
- Creates a binary field mask for SPOT blocks.
- Creates a binary flood mask for Pléiades/SPOT Reflectance blocks.
- Calculates NDVI, EVI, SAVI, NDWI
- Machine Learning based algorithm for Built up area detection.
- Performs histogram normalization and denoising (optional) on one (or more) images in preparation for further processing such as change detection.
- Detects man-made infrastructure changes across two different images with a high accuracy.
- Detects man-made infrastructure changes across two different images with a high accuracy.
- Runs a simple unsupervised K-means clustering algorithm for classification.
- Classifies imagery into discrete land cover classes.
- Computes Land Surface Temperature over Sentinel-2 imagery.
- Detect trucks, buses, tractor-trailers and other land-based large vehicles.
- Calculates Moisture Stress Index(MSI) from satellite images.
- Calculates NDVI on imagery with RGB and NIR bands.
- Differentiates NDVI values into vegetation classes.
- Converts NetCDF to GeoTIFF format.
- Calculates Normalized Burnt Index(NBI) from satellite images.
- Calculates Normalized Difference Vegetation Index (NDVI).
- Calculates Normalized Difference Water Index(NDWI) from satellite images.
- Pansharpens images from Pléiades / SPOT Reflectance (Download) or Sentinel-2 L2A Analytic (GeoTIFF).
- This algorithm improves the spatial resolution of Pléiades Neo image from 0.3 m to 0.1 m (by factor of 3).
- Calculate a radio frequency (RF) viewshed from a transmitter location over a Digital Elevation Model
- Transforms input data to the desired coordinate reference system (CRS).
- Clips rasters into tiles for machine learning algorithms.
- Extracts zonal statistics from a raster image.
- The block provides a more reliable identification of clouds in Sentinel-2 imagery than the cloud mask data included with the L1C and L2A products from ESA. The block processes a granule and generates an image with the same coverage indicating whether pixels are affected by clouds.
- Generates water extent maps for lakes and reservoirs within the given bounding box and time range.
- Settlement Mapping Block for developing countries in order to track dwelling count, dwelling size and settlement expansion to measure sustainable development goals. This block takes Pleiades input data and outputs a mask of dwellings.
- Finds shadows of objects in the image and for each pixel returns the probability of it to be a part of a shadow.
- Enhances the sharpness of a raster image by applying an unsharp mask filter algorithm.
- Ships at sea Detection detects ships in SPOT images from the SPOT 6/7 Display Block.
- Fuses AIS properties with the ship detection block output geometries.
- Generate an RGBA Image where the histogram of colors is equalized.
- Detects small vehicles on satellite images with 0.5m GSD.
- Calculates Soil Adjusted Vegetation Index(SAVI) from satellite images.
- Detects baseball fields, tennis courts, soccer fields, and stadiums in cities and towns.
- Storage Tank Detection detects oil storage tanks locations on SPOT Display imagery.
- Quadruples imagery resolution of Pléiades or SPOT.
- Runs a deep-learning based superresolution algorithm to create a Sentinel-2 image with 10 m resolution across all bands.
- Automated Image Anomaly Detection System. Finds Spectrographic anomalies by using Temporal Change Detection.
- Converts TerraSAR EEC data to GeoTIFF data format and optionally clips the image.
- Apply image statistics on stack of rasters.
- Finds trees in the image and for each pixel returns the probability map of it to be a part of a tree.
- Finds trees location and their height in the input image.
- Orbital Insight’s truck detection algorithm uses wide area object detection in Pleiades imagery to accurately identify and quantify trucks. This saves analysts significant amounts of time in conducting pattern of life analyses and activity based intelligence.
- Outputs a binary image where white pixels represent buildings.
- Increase spatial resolution of urban locations by 4 times.
- Transforms rasters into vectors.
- Calculate a viewshed from an observer location over a Digital Elevation Model
- Creates a binary water mask for Pléiades/SPOT Reflectance blocks.
- Wind Turbine Detection detects wind turbines locations on SPOT Display imagery.
- The multiresolution segmentation algorithm consecutively merges pixels. Thus it is a bottom-up segmentation algorithm based on a pairwise region merging technique. Multiresolution segmentation is an optimization procedure which, for a given number of image objects, minimizes the average heterogeneity and maximizes their respective homogeneity. This homogeneity criterion is defined as a combination of spectral homogeneity and shape homogeneity.