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@ClarkCGA

Clark Center for Geospatial Analytics

Center for Geospatial Analytics at Clark University
README.md

Clark Center for Geospatial Analytics (Clark CGA, formerly Clark Labs) is an interdisciplinary research center at Clark University in Worcester, MA, USA established to catalyze synergistic research that uses geospatial analytics to address pressing issues of global environmental change. Clark CGA plays an entrepreneurial role at the forefront of the rapidly evolving fields of geospatial analytics and GeoAI.

The mission of Clark CGA is to drive the progress of geospatial analytics for sustainable environmental stewardship and broader societal benefits. Through innovative scientific research, we develop pioneering geospatial software and technology. Collaborating across disciplines, we address diverse challenges in conservation, climate change impacts, land change modeling, and environmental sustainability. By advancing application tools and technology, our aim is to lead the frontier of geospatial capabilities for societal advancement and environmental resilience.

News

The latest version of TerrSet Geospatial Monitoring and Modeling Software, liberaGIS, is now available for free. Check TerrSet repository to learn more and download the software.

Contact

Check our website to learn more about our work: https://clarku.edu/cga.

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  1. TerrSet TerrSet Public

    TerrSet Geospatial Monitoring and Modeling Software

    40 2

  2. UDef-ARP UDef-ARP Public

    UDef-ARP was developed by Clark Labs, in collaboration with TerraCarbon, to facilitate implementation of the Verra tool, VT0007 Unplanned Deforestation Allocation (UDef-A).

    Python 30 16

  3. multi-temporal-crop-classification-baseline multi-temporal-crop-classification-baseline Public

    Baseline model for crop type segmentation as part of the HLS FM downstream task evaluations

    Python 22 7

  4. multi-temporal-crop-classification-training-data multi-temporal-crop-classification-training-data Public

    This repository contains the pipeline for generating a training dataset for land cover and crop type segmentation using USDA CDL data.

    Jupyter Notebook 10 4

  5. cloud-gap-filling-td cloud-gap-filling-td Public

    Training data generation for cloud gap imputation fine-tuning of Prithvi Geospatial Foundation Model

    Jupyter Notebook 8 3

  6. gfm-gap-filling gfm-gap-filling Public

    Fine-tuning the GFM model for gap filling task.

    Jupyter Notebook 2 1

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