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  • Coming back soon: currently not available. An algorithm that detects and classifies multiple aircraft types (e.g., carrier, fighter, helicopter, small aircraft, and others) in Pléiades imagery using wide-area object detection. Uses cases include aircraft management, airport monitoring, and environmental modelling.
  • Coming back soon: currently not available. Finds bounding boxes around clearly separated buildings.
  • Coming back soon: currently not available. Detect apartments, houses, industrial buildings, and sheds in satellite images.
  • Coming back soon: currently not available. An algorithm that detects buildings in SPOT or Pléiades display imagery and returns a probability map. It identifies all pixels corresponding to buildings in satellite images. Building detection can be used for urban planning, construction, fire risk estimation, land use management, vegetation management, and powerline inspection.
  • Coming back soon: currently not available. Burnt Area Extraction by temporal analysis for forest fires.
  • Coming back soon: currently not available. An algorithm that detects and quantifies cars in Pléiades imagery using wide-area object detection. It can be used to conduct pattern-of-life analyses and activity-based intelligence. Car detection can be used for traffic and parking management, retail analysis, and urban planning.
  • Coming back soon: currently not available. 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.
  • Coming back soon: currently not available. Detects changes between two images and returns a change map.
  • Coming back soon: currently not available. Provides cloud masks for Pléiades and SPOT imagery.
  • The cloud-native asset model (CNAM) enhances all UP42 assets from a tasking or catalog order by transforming them into individual geospatial features that are ready for immediate download. Regardless of provider or delivery format, UP42 automatically transforms assets into a standard model, without any additional steps. This common model is further improved by converting raster data into cloud-optimized GeoTIFFs and vector masks into GeoJSONs. CNAM ensures interoperability and eases integration by removing the need to learn different data formats, delivery structures, and naming conventions. Users now have a simplified process of searching, visualizing, and downloading data.
  • Coming back soon: currently not available. Performs automatic detection and correction of sub-pixel misalignments between remote sensing datasets.
  • Coming back soon: currently not available. Detection and matching of features from satellite images.
  • Coming back soon: currently not available. Calculates Enhanced Vegetation Index(EVI) from satellite images.
  • Coming back soon: currently not available. Shows farmers their intra-field variability in fertilizer needs.
  • Coming back soon: currently not available. An algorithm that detects agricultural field boundaries in Central Europe from SPOT imagery. The algorithm automatically delineates agricultural fields visible in a satellite image, producing a layer of polygons to describe them. Field detection can be used for environmental monitoring, disaster response, and land cover classification.
  • Coming back soon: currently not available. An algorithm that detects agricultural field boundaries in Iowa from SPOT imagery. The algorithm automatically delineates agricultural fields visible in a satellite image, producing a layer of polygons to describe them. Field detection can be used for environmental monitoring, disaster response, and land cover classification.
  • Coming back soon: currently not available. Creates a binary flood mask for Pléiades/SPOT reflectance data.
  • Coming back soon: currently not available. Calculates NDVI, EVI, SAVI, NDWI.
  • Coming back soon: currently not available. Machine learning based algorithm for built up area detection.
  • Coming back soon: currently not available. Performs histogram normalization and denoising (optional) on one (or more) images in preparation for further processing such as change detection.
  • Coming back soon: currently not available. Detects man-made infrastructure changes across two different images with a high accuracy.
  • Coming back soon: currently not available. Detects man-made infrastructure changes across two different images with a high accuracy.
  • Coming back soon: currently not available. Runs a simple unsupervised K-means clustering algorithm for classification.
  • Coming back soon: currently not available. Classifies imagery into discrete land cover classes.
  • Coming back soon: currently not available. Computes land surface temperature over Sentinel-2 imagery.
  • Coming back soon: currently not available. Detect trucks, buses, tractor-trailers and other land-based large vehicles.
  • Coming back soon: currently not available. Calculates Moisture Stress Index(MSI) from satellite images.
  • Coming back soon: currently not available. Calculates Normalized Burnt Index(NBI) from satellite images.
  • Coming back soon: currently not available. Calculates Normalized Difference Vegetation Index (NDVI).
  • Coming back soon: currently not available. Calculates Normalized Difference Water Index(NDWI) from satellite images.
  • Pansharpening is an image fusion technique that merges high resolution panchromatic images with lower-resolution multispectral images to yield a single high spatial and high spectral resolution image. With UP42, you can easily convert your data into a pansharpened product through a streamlined process that requires only a few clear steps.
  • Coming back soon: currently not available. This algorithm improves the spatial resolution of Pléiades Neo image from 0.3 m to 0.1 m (by factor of 3).
  • Coming back soon: currently not available. The algorithm calculates a radio frequency (RF) viewshed from a transmitter location over a Digital Elevation Model.
  • Coming back soon: currently not available. Extracts zonal statistics from a raster image.
  • Coming back soon: currently not available. The algorithm 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 algorithm processes a granule and generates an image with the same coverage indicating whether pixels are affected by clouds.
  • Coming back soon: currently not available. An algorithm that detects shadows in SPOT or Pléiades imagery and returns a probability map. It identifies all pixels corresponding to shadows in satellite images. Shadow detection can be used for urban planning, construction, solar power planning, economic development tracking, and land use analysis.
  • Coming back soon: currently not available. An algorithm that detects ships in SPOT display imagery using object detection. The algorithm is best suited to detect medium and large ships larger than 26 m, but can also detect smaller ships in clear imagery. Ship detection can be used for maritime surveillance, port and harbor management, and fishing activity detection.
  • Coming back soon: currently not available. Fuses AIS properties with the ship detection block output geometries.
  • Coming back soon: currently not available. Generate an RGBA Image where the histogram of colors is equalized.
  • Coming back soon: currently not available. Detects small vehicles on satellite images with 0.5m GSD.
  • Coming back soon: currently not available. Calculates Soil Adjusted Vegetation Index(SAVI) from satellite images.
  • Coming back soon: currently not available. Detects baseball fields, tennis courts, soccer fields, and stadiums in cities and towns.
  • Coming back soon: currently not available. An algorithm that detects oil storage tanks in SPOT imagery using object detection. Storage tank detection can be used for oil industry monitoring, infrastructure management, and construction monitoring.
  • Coming back soon: currently not available. Automated Image Anomaly Detection System. Finds spectrographic anomalies by using temporal change detection.
  • Coming back soon: currently not available. Applies image statistics on a stack of rasters.
  • Coming back soon: currently not available. An algorithm that detects trees in SPOT or Pléiades imagery and returns a probability map and the height of detected trees. For each pixel, it returns the probability of it to be part of a tree, and computes its height based on the length of its adjacent shadow. Tree and tree height detection can be used for infrastructure vegetation risk monitoring, urban planning, and construction.
  • Coming back soon: currently not available. An algorithm that detects trees in SPOT or Pléiades imagery and returns a probability map. Tree Detection identifies all pixels corresponding to trees in satellite images. Tree detection can be used for infrastructure vegetation risk monitoring, urban planning, construction, fire risk estimation, land management, powerline and trainline inspection, and forestry.
  • Coming back soon: currently not available. An algorithm that detects and quantifies trucks in Pléiades imagery using wide area object detection. Truck detection can be used for supply chain and logistics management, traffic management, and urban planning.
  • Coming back soon: currently not available. Outputs a binary image where white pixels represent buildings.
  • Coming back soon: currently not available. Increase spatial resolution of urban locations by 4 times.
  • Coming back soon: currently not available. Calculate a viewshed from an observer location over a Digital Elevation Model.
  • Coming back soon: currently not available. Creates a binary water mask for Pléiades/SPOT Reflectance data.
  • Coming back soon: currently not available. An algorithm that detects wind turbines in SPOT imagery using object detection. Wind turbine detection can be used for energy production monitoring, aircraft traffic regulations, infrastructure management, and construction monitoring.
  • Coming back soon: currently not available. 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.

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