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@ClarkCGA

Clark Center for Geospatial Analytics

Center for Geospatial Analytics at Clark University

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.

Contact

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

Follow Clark CGA on LinkedIn or sign up to our newsletter to hear more about our work.

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  1. TerrSet TerrSet Public

    TerrSet Geospatial Monitoring and Modeling Software

    52 6

  2. gelos-app gelos-app Public

    Web App for Geospatial Exploration of Latent Observation Space (GELOS)

    JavaScript 3

  3. 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 36 17

  4. drc_30x30_opt drc_30x30_opt Public

    DRC's 30 x 30 Spatial Planning Toolbox

    Python

  5. 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 24 7

  6. eofm-tutorial eofm-tutorial Public

    A tutorial to train a diffusion model using self-supervised learning technique

    Python

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