We describe a modern C++20/Python toolbox for feature extraction and geospatial data manipulation developed at the Center for Space Research and used for a variety of data processing applications in our lab. The toolbox provides powerful feature extraction tools in a suite of flexible, modular applications that can be used to compose geospatial data processing pipelines. The toolbox is exposed through a web API and is planned for release later this year.
Global digital elevation models (DEMs) generated from spaceborne synthetic aperture radar (SAR), such as the Copernicus 30 m DEM , provide exceptional coverage of the Earth's topography. However, SAR-derived DEMs struggle to accurately map terrain under forest canopies and in certain topographic conditions. In contrast, spaceborne laser altimeters like ICESat-2 can accurately measure ground elevations in areas where SAR sensors struggle, but the lack of dense coverage from laser altimetry precludes creation of complete global DEMs. This work aims to combine the accuracy of laser altimetry with the coverage of SAR by using deep learning algorithms. A convolutional neural network (CNN) is trained to correct Copernicus 30 m DEM using sparse but accurate ICESat-2 elevations in the south-east United States around South Carolina. Model inputs include temporally coincident imagery from Sentinel-2A, other SAR inputs from Sentinel-1B, as well as Copernicus 30 m DEM. The CNN is trained to correct the elevation of each individual pixel, allowing for the use of sparse ICESat-2 measurements. This allows the creation of a global DEM with the coverage of SAR and precision closer to that of laser altimetry. The resulting CNN model reduced ground elevation RMSE from 8.65 m to 2.62 m. The corrected DEM has potential to benefit numerous scientific endeavors requiring accurate global topographic information.
We present a Simple POint Cloud (SPOC) file format, suitable for efficiently storing and processing geospatial point cloud data. This format provides support for 64-bit floating-point precision coordinates, compressed storage, and data streaming. The code base is implemented as a header-only, modern C++ library with Python extensions under an open source license. The format can be applied in a wide variety of use case scenarios, and was motivated by a need for high-precision, transparent data storage and transmission for geospatial processing and machine learning applications. Existing file formats sometimes either do not support the precision and dynamic range necessary for certain applications, they do not support common interprocess communication protocols, or they are overly complex or rigid.
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