Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep learning methods are particularly promising for modeling many remote sensing tasks given the success of deep neural networks in similar computer vision tasks and the sheer volume of remotely sensed imagery available. However, the variance in data collection methods and handling of geospatial metadata make the application of deep learning methodology to remotely sensed data nontrivial. For example, satellite imagery often includes additional spectral bands beyond red, green, and blue and must be joined to other geospatial data sources that may have differing coordinate systems, bounds, and resolutions. To help realize the potential of deep learning for remote sensing applications, we introduce TorchGeo, a Python library for integrating geospatial data into the PyTorch deep learning ecosystem. TorchGeo provides data loaders for a variety of benchmark datasets, composable datasets for uncurated geospatial data sources, samplers for geospatial data, and transforms that work with multispectral imagery. TorchGeo is also the first library to provide pre-trained models for multispectral satellite imagery (e.g., models that use all bands from the Sentinel-2 satellites), allowing for advances in transfer learning on downstream remote sensing tasks with limited labeled data. We use TorchGeo to create reproducible benchmark results on existing datasets and benchmark our proposed method for preprocessing geospatial imagery on the fly. TorchGeo is open source and available on GitHub: https://github.com/microsoft/torchgeo.

Adam J. Stewart

Caleb Robinson

Isaac A. Corley

Anthony Ortiz

Juan M. Lavista Ferres

Arindam Banerjee

ACM Transactions on Spatial Algorithms and Systems

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.

ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes high-quality papers on all aspects of spatial algorithms and systems, as well as closely related disciplines. It adopts a multidisciplinary perspective, covering a wide range of areas where spatial data is manipulated or visualized. These areas include geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, along with related visual computing fields such as computer graphics, computer vision, geometric modeling, and visualization, where spatial, geospatial, and spatiotemporal data are central.

《ACM Transactions on Spatial Algorithms and Systems》是一本学术期刊，致力于发表关于空间算法和系统及相关学科的高质量论文。该期刊具有多学科的视角，涵盖了多个领域，其中空间数据的处理或可视化是核心内容。这些领域包括地理学、地理信息系统（GIS）、地理空间和时空数据库、空间和度量索引、基于位置的服务、基于网络的空间应用、地理信息检索（GIR）、空间推理与挖掘、安全与隐私，以及与计算机图形学、计算机视觉、几何建模和可视化等相关的视觉计算领域。

TorchGeo: Deep Learning With Geospatial Data

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Journal Info

Category	Quartile
REMOTE SENSING	4