PyWPS Demo server PyWPS testing and development server. Do NOT use this server in production environment. You shall setup PyWPS as WSGI application for production. Please refer documentation for further detials. WPS GRASS PyWPS Demo Dev theme WPS 1.0.0 2.0.0 None None Treemetrics Alex Poveda Developer +00 00 00 00 Cork T12 Y275 Ireland info@treemetrics.com get_access_token_from_sentinel_hub Get SentinelHub access token Get SentinelHub access token. vegeation_indices Compute Vegetation Indices & Band Composites Compute NDVI, EVI, IRB, IRP images from the input image. get_up42_catalog_data Get UP42 catalog data Sorts records first by cloud cover and then by date and time. check_quota_from_up42 Get UP42 quota data Get UP42 quota data. get_sentinel_catalog_data Get sentinel catalog data Sorts records first by cloud cover and then by date and time. download_image_from_s2dr3 S2DR3 Download Image Download image from S2DR3. raster_to_cog Raster to COG This process optimise raster for cloud visualisation including: (1) Conversion to 8 bits (0-255), (2) Stretch image histogram for better contrast visualisation (optional), (3) Create Alpha channel for performance with Geoserver, (4) Image compression, (6) COG gdal default parameters fo tiled data, pyramid layers, etc download_image_from_provider Download Image from integrated provider Download image from integrated provider. get_fs_registry_catalog_data Get catalog data from Forest Spatial registry V1 Process to get catalog data from Forest Spatial registry download_image_from_fs_registry FS registry Download Image Download image from FS registry. get_s2_image Sentinel2 Image Get the RGB and NDVI image with less cloud cover for the defined season healthcheck Healthcheck Operation status of ForestSpatial. download_image_from_up42 UP42 Download Image Download image from UP42. show_forest_on_the_thumbnail Get full catalog data Process to show forest on the thumbnail as a point. rf_canopy Forest Canopy Area This process uses Random Forest model for binary forest canopy segmentation. The service uses the Random Forest model and the user provided "Point Labels" to perform and image segmentation. The images allowed are 4 bands RGBN with spatial resolution around 0.3-0.6m. The "Point Labels" should contain values of Forest or Non-Forest for each point. download_image_from_sentinel_hub SentinelHub Download Image Download image from Sentinel Hub. get_full_catalog_data Get full catalog data Process to choose between UP42 and Sentinel processes based on selected sensors. sam_canopy Forest Canopy Area This process uses SAM model for binary forest canopy segmentation. The service uses the SAM model and the user provided "Point Labels" to perform and image segmentation. The images allowed are 3 bands RGB with spatial resolution around 0.3-0.6m. The "Point Labels" should contain values of Forest or Non-Forest for each point. After the first run, if the precomputed SAM embeddings are available in the output directory, the service allows to use new "Point Labels" and the precomputed SAM embeddings of a given image without need of performing a full computation (faster). _process_example Example process This process is an example to show the different types or inputs and return a raster and a string outputs check_quota_from_sentinel_hub Get sentinel hub quota data Get sentinel hub quota data. dino_strata Forest Stand Stratification This process does Multi-class strata segmentation. The service can run a new image or using precomputed DINO features, if available. The service uses the DINO model and user provided "Point Labels" to perform and image segmentation. The images allowed are 3 bands RGB with spatial resolution around 0.3-0.6m. After the first run, if the precomputed DINO features are available in the output directory, the service allows to use new "Point Labels" and the precomputed DINO features of a given image without need of performing a full computation (faster). sam_canopy_change Forest Canopy Area Change The Forest Change Detection Service is designed to identify changes in forest canopy between two high-resolution satellite images within a specified area of interest. It takes two input images (e.g., the oldest and newest images), an area of interest (AOI), and Forest/Non-Forest labeled points as input. The service performs a change detection analysis, generating Forest Masks and a Change file representing the detected changes. rf_canopy_change Forest Canopy Area Change The Forest Change Detection Service is designed to identify changes in forest canopy between two high-resolution satellite images within a specified area of interest. It takes two input images (e.g., the oldest and newest images), an area of interest (AOI), and Forest/Non-Forest labeled points as input. The service performs a change detection analysis, generating Forest Masks and a Change file representing the detected changes. last_year_change Last year vegetation and forest removal Identification of patches of vegetation that have been removed within the last year based on Sentinel-2 images. general_vegetation_classification General Vegetation Classification General vegetation classification based on NDVI, as defined in pseudo Forest Canopy Density (FCD) script available in SentinelHub en-US en-US