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Geospatial / Feed

Geospatial data and analysis is critical for conservation, from planning to implementation and measuring success. The Geospatial group focuses on all aspects of this field, from field surveys to remote sensing and data development/analysis to GIS systems.

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Vance Russell
Over 35 years of experience in biodiversity conservation worldwide, largely focused on forests, rewilding and conservation technology. I run my own business assisting nonprofits and agencies in the conservation community
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Born Free Uses Smart Maps to Save Lions, Solve Human-Wildlife Conflict

Esther Githinji

17 August 2023 2:35pm

Location intelligence helps Born Free Kenya understand where communities and lions are in conflict. Outreach to fortify cattle corrals is guided by smart maps and spatial analysis.

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Conservation StoryMap competition

Adrian Hughes

17 July 2023 12:52pm

ESRI and IUCN have teamed up to launch a StoryMap competition. Storytellers are invited to share stories on a range of conservation topics including technological innovation, ecological corridors and initiatives by...

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Monitoring large wildlife populations with ML and Satellites

Ellie Warren

11 July 2023 7:28pm

Here's an interesting new paper in Nature about how new techniques in machine learning and satellite remote sensing are allowing researchers to monitor and count large populations of migratory terrestrial mammals like elephants and zebras.

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Read "Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape | Nature Communications" on Nature

Deep learning enables satellite-based monitoring of large populations of terrestrial mammals across heterogeneous landscape | Nature Communications

New satellite remote sensing and machine learning techniques offer untapped possibilities to monitor global biodiversity with unprecedented speed and precision. These efficiencies promise to reveal novel ecological insights at spatial scales which are germane to the management of populations and entire ecosystems. Here, we present a robust transferable deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine-resolution (38-50 cm) satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types, with an overall F1-score of 84.75% (Precision: 87.85%, Recall: 81.86%). This research demonstrates the capability of satellite remote sensing and machine learning techniques to automatically and accurately count very large populations of terrestrial mammals across a highly heterogeneous landscape. We also discuss the potential for satellite-derived species detections to advance basic understanding of animal behavior and ecology. This study presents a deep learning pipeline to automatically locate and count large herds of migratory ungulates (wildebeest and zebra) in the Serengeti-Mara ecosystem using fine resolution satellite imagery. The results achieve accurate detection of nearly 500,000 individuals across thousands of square kilometers and multiple habitat types.

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The role of low-code Earth observation tools in accelerating the sustainability transition

Cassie Anderson

30 June 2023 2:13pm

We've published a paper on the role of low-code Earth observation tools in the sustainability transition. It covers how different roles in a business like analysts and executives can benefit from this tech and how it can be applied to ESG, Nature Based Solutions and insurance...

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