Tools tagged with "Sentinel-2"

This plugin seamlessly integrates Google Earth Engine with QGIS, providing users with powerful geospatial analysis capabilities. It leverages the Google Earth Engine Python API to enable advanced remote sensing and data processing directly within the QGIS environment.

CoastSat is an open-source Python software toolkit designed for global shoreline mapping. It enables users to obtain time-series of shoreline position at any coastline worldwide. The tool leverages 40 years (and growing) of publicly available satellite imagery from Landsat and Sentinel-2 missions.

AnytimeFormer is a deep learning model designed to reconstruct land surface reflectance at any given time by effectively fusing irregular and asynchronous Synthetic Aperture Radar (SAR) and optical time series data. It addresses the challenge of combining diverse satellite data streams to provide continuous, high-quality environmental monitoring. The project offers a robust solution for generating comprehensive reflectance products from disparate remote sensing observations.

This dataset, hosted by the ETH Zurich Research Collection, provides a global canopy height map for 2020. It was created using data from Sentinel-2 and GEDI, offering valuable insights into forest structures and biomass. The resource is open access and available under a Creative Commons license.

The Copernicus Browser offers a comprehensive platform for exploring and acquiring high-resolution images from the entire suite of Copernicus missions. Users can easily navigate to areas of interest, specify time ranges and data collections, and visualize the resulting data directly within the browser. It supports various visualisations, high-resolution image downloads, timelapse creation, and raw data access for local processing.

sits is an open-source R/Python package designed for satellite image time series analysis. It enables users to apply machine learning techniques for classifying image time series obtained from Earth observation data cubes, providing a complete workflow.

Google Earth Engine provides a comprehensive data catalog for scientists, researchers, and developers. It combines a vast collection of satellite imagery and geospatial datasets with powerful analysis tools. Users can detect changes, map trends, and quantify differences on the Earth's surface at a planetary scale.

Sentle is an open-source Python package designed for downloading and processing Sentinel-1 and Sentinel-2 satellite data cubes at a large scale, even exceeding memory limits. It provides functionalities for cloud detection, snow masking, data harmonization, merging, and generating temporal composites from the imagery. The tool aims to simplify the complex workflow of preparing satellite data for analysis across diverse applications.

Copernicus Browser is a powerful open-source web application designed for searching, visualizing, and analyzing satellite imagery from Sentinel-1, 2, 3, and 5P missions. It enables users to explore vast Earth Observation datasets, tweak rendering parameters, and download raw data or create custom visualizations. The platform is an integral part of the Copernicus Data Space Ecosystem, providing free access to advanced geospatial features for end-users.

This dataset offers Digital Earth Africa Sentinel-2 Level-2A bottom-of-atmosphere reflectance data covering the African continent. Derived from the European Union Copernicus Sentinel-2 mission, it provides analysis-ready data ideal for various research and applications. New scenes are added regularly, usually within hours of their availability from Copernicus OpenHub.

The Sentinel Application Platform (SNAP) provides a unified architecture for all Sentinel Toolboxes, enabling efficient processing and analysis of Earth Observation data. It leverages technological innovations such as a modular rich client platform, generic EO data abstraction, and a graph processing framework. This platform is designed for extensibility and portability, facilitating advanced geospatial data manipulation.

Satpy is an open-source Python library designed for reading, processing, and visualizing data from various earth-observing satellites. It provides a flexible framework for handling different satellite instruments, enabling users to generate composites, apply atmospheric corrections, and perform re-sampling operations. This tool is invaluable for researchers and developers working with meteorological and environmental satellite imagery.

This AWS registry provides free and open access to Sentinel-2 satellite imagery, a land monitoring mission offering high-resolution optical data globally every five days. It includes both L1C and L2A data, regularly updated within hours of availability. The data supports various ongoing studies and applications.

WorldCereal is a research project funded by the European Space Agency, dedicated to providing a free, cloud-based processing system. This system generates high-resolution custom crop location and type products at both local and global scales. It aims to enhance food security through advanced geospatial data and analysis.

Satlas Super Resolution offers globally generated AI-enhanced imagery for 2023, aiming for high accuracy and frequent updates. This GitHub repository provides the training and inference code, data, and model weights for the super-resolution outputs. It supports the mission of Satlas to provide open, AI-generated geospatial data.

EOReader is a powerful open-source Python library designed for remote sensing applications. It provides a sensor-agnostic framework for reading and processing data from a wide range of optical and Synthetic Aperture Radar (SAR) satellite constellations. Users can efficiently load and stack various components such as spectral bands, cloud masks, Digital Elevation Models (DEMs), and spectral indices, simplifying complex satellite imagery workflows.

NASA's Applied Remote Sensing Training (ARSET) program provides free online and in-person webinars and workshops. It aims to empower individuals and organizations to leverage Earth-observing data for informed environmental management and decision-making across various applications globally.

OmniCloudMask is a Python library designed for state-of-the-art cloud and cloud shadow segmentation in high to moderate resolution satellite imagery. It offers higher accuracy and supports a wide range of resolutions, sensors, and processing levels, validated on data from Sentinel-2, PlanetScope, Landsat, and Maxar.

This dataset provides Sentinel-2 Level-2A satellite imagery in Cloud-Optimized GeoTIFF (COG) format, offering high-resolution optical data for land monitoring. It includes global coverage with frequent updates, accessible via AWS, and is accompanied by SpatioTemporal Asset Catalog (STAC) metadata, making it easily discoverable and usable for various geospatial applications.

The Copernicus Browser offers a platform to browse and download full resolution images from all Copernicus missions. Users can select an area of interest, time range, and collections to inspect data, create visualizations, generate timelapses, or download raw data for local processing.

This GitHub repository compiles a comprehensive list of open geospatial datasets hosted on Amazon Web Services (AWS). It serves as a valuable resource for developers, researchers, and data scientists seeking readily available geospatial data for various applications and projects, streamlining access to critical information.

WisconsinView serves as a central hub for accessing and utilizing remote sensing imagery and LiDAR data specifically for the state of Wisconsin. This initiative facilitates the discovery and download of valuable geospatial datasets through its partnership with GeoData@Wisconsin. It aims to support various applications by providing resources and educational content related to satellite information and remote sensing.

This GitHub repository serves as a comprehensive catalog of open geospatial datasets. It organizes and lists data accessible through prominent platforms such as AWS, Google Earth Engine, Microsoft Planetary Computer, NASA CMR, and STAC Index, providing a valuable resource for geospatial professionals.

The FloodPlanet dataset provides manually labeled inundation data, meticulously collected for advancing flood detection research. It serves as a crucial resource for training and validating deep learning models, utilizing high-resolution imagery from PlanetScope, Sentinel-1, and Sentinel-2 satellites. This version 1.0 dataset, contributed by Zhijie(JJ) Zhang and collaborators, is a valuable asset for the geospatial community.

Pytroll is an easy-to-use, modular, free, and open-source Python framework designed for the processing of Earth Observation (EO) satellite data. Its packages are suitable for both research and development (R&D) environments and 24/7 operational production. The framework focuses on atmospheric applications and imaging sensors, supporting a wide range of Earth sciences.

EOxCloudless provides high-resolution, cloud-free satellite imagery derived from the ESA Copernicus Sentinel-2 mission. These elaborately post-processed images are optimized for various applications, including web maps and desktop GIS tools. The service offers both prerendered map caches and source mosaics for diverse user needs.

Orfeo ToolBox (OTB) is a powerful C++ library designed for processing satellite images and other high-resolution geospatial data. It offers a wide array of image processing algorithms, including classification, segmentation, filtering, and geometric corrections, making it a robust solution for remote sensing applications.

NASA's Land Processes Distributed Active Archive Center (LP DAAC) offers a vast array of Earth science data. This center is essential for researchers and scientists investigating, characterizing, and monitoring biological, geological, hydrological, and ecological conditions and processes. It serves as a key resource within the NASA Earthdata ecosystem for land science.