{ "_id": "6a104dffacfb0bcc41ca088f", "Package": "geodl", "Type": "Package", "Title": "Geospatial Semantic Segmentation with Torch and Terra", "Version": "0.3.0", "Date": "2025-11-08", "Authors@R": "c(\nperson(family=\"Maxwell\", given=\"Aaron\", email=\"Aaron.Maxwell@mail.wvu.edu\", role=c(\"aut\", \"cre\", \"cph\")),\nperson(family=\"Farhadpour\", given=\"Sarah\", email=\"sf00039@mix.wvu.edu\", role=c(\"aut\")),\nperson(family=\"Das\", given=\"Srinjoy\", email=\"srinjoy.das@mail.wvu.edu\", role=c(\"aut\")),\nperson(family=\"Yang\", given=\"Yalin\", email=\"yalin.yang@mail.wvu.edu\", role=c(\"aut\")))", "Maintainer": "Aaron Maxwell ", "Description": "Provides tools for semantic segmentation of geospatial\ndata using convolutional neural network-based deep learning.\nUtility functions allow for creating masks, image chips, data\nframes listing image chips in a directory, and DataSets for use\nwithin DataLoaders. Additional functions are provided to serve\nas checks during the data preparation and training process. A\nUNet architecture can be defined with 4 blocks in the encoder,\na bottleneck block, and 4 blocks in the decoder. The UNet can\naccept a variable number of input channels, and the user can\ndefine the number of feature maps produced in each encoder and\ndecoder block and the bottleneck. Users can also choose to (1)\nreplace all rectified linear unit (ReLU) activation functions\nwith leaky ReLU or swish, (2) implement attention gates along\nthe skip connections, (3) implement squeeze and excitation\nmodules within the encoder blocks, (4) add residual connections\nwithin all blocks, (5) replace the bottleneck with a modified\natrous spatial pyramid pooling (ASPP) module, and/or (6)\nimplement deep supervision using predictions generated at each\nstage in the decoder. A unified focal loss framework is\nimplemented after Yeung et al. (2022)\n. We have also\nimplemented assessment metrics using the 'luz' package\nincluding F1-score, recall, and precision. Trained models can\nbe used to predict to spatial data without the need to generate\nchips from larger spatial extents. Functions are available for\nperforming accuracy assessment. The package relies on 'torch'\nfor implementing deep learning, which does not require the\ninstallation of a 'Python' environment. Raster geospatial data\nare handled with 'terra'. Models can be trained using a Compute\nUnified Device Architecture (CUDA)-enabled graphics processing\nunit (GPU); however, multi-GPU training is not supported by\n'torch' in 'R'.", "License": "GPL (>= 3)", "URL": "https://github.com/maxwell-geospatial/geodl,\nhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0315127,\nhttps://wvview.org/gslr/index.html", "BugReports": "https://github.com/maxwell-geospatial/geodl/issues", "NeedsCompilation": "no", "Encoding": "UTF-8", "RoxygenNote": "7.3.3", "Roxygen": "list(markdown = TRUE)", "VignetteBuilder": "knitr", "Config/pak/sysreqs": "libabsl-dev cmake libfreetype6-dev libgdal-dev\ngdal-bin libgeos-dev libglu1-mesa-dev make texlive libjpeg-dev\nlibpng-dev libtiff-dev libuv1-dev libgl1-mesa-dev libssl-dev\nlibproj-dev libsqlite3-dev libudunits2-dev libx11-dev\nzlib1g-dev", "Repository": "https://maxwell-geospatial.r-universe.dev", "Date/Publication": "2025-11-10 23:32:30 UTC", "RemoteUrl": 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