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• HawkEars: a high-performance bird sound classifier for Canada

HawkEars is a deep learning model designed specifically to recognize the calls of 328 Canadian bird species and 13 amphibians.

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• Can CBIs promote coexistence? A Case Study from Northern Tanzania

Can conservation-based incentives promote the willingness of local communities to coexist with wildlife? A case of Burunge Wildlife Management Area, Northern Tanzania

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• Maps, Education and Community

geocarto

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• Application of computer vision for off-highway vehicle route detection: A case study in Mojave desert tortoise habitat

Driving off-highway vehicles (OHVs), which contributes to habitat degradation and fragmentation, is a common recreational activity in the United States and other parts of the world, particularly in desert environments with fragile ecosystems. Although habitat degradation and mortality from the expansion of OHV networks are thought to have major impacts on desert species, comprehensive maps of OHV route networks and their changes are poorly understood. To better understand how OHV route networks have evolved in the Mojave Desert ecoregion, we developed a computer vision approach to estimate OHV route location and density across the range of the Mojave desert tortoise (Gopherus agassizii). We defined OHV routes as non-paved, linear features, including designated routes and washes in the presence of non-paved routes. Using contemporary (n = 1499) and historical (n = 1148) aerial images, we trained and validated three convolutional neural network (CNN) models. We cross-examined each model on sets of independently curated data and selected the highest performing model to generate predictions across the tortoise's range. When evaluated against a ‘hybrid’ test set (n = 1807 images), the final hybrid model achieved an accuracy of 77%. We then applied our model to remotely sensed imagery from across the tortoise's range and generated spatial layers of OHV route density for the 1970s, 1980s, 2010s, and 2020s. We examined OHV route density within tortoise conservation areas (TCA) and recovery units (RU) within the range of the species. Results showed an increase in the OHV route density in both TCAs (8.45%) and RUs (7.85%) from 1980 to 2020. Ordinal logistic regression indicated a strong correlation (OR = 1.01, P < 0.001) between model outputs and ground-truthed OHV maps from the study region. Our computer vision approach and mapped results can inform conservation strategies and management aimed at mitigating the adverse impacts of OHV activity on sensitive ecosystems.

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• HawkEars: a high-performance bird sound classifier for Canada

HawkEars is a deep learning model designed specifically to recognize the calls of 328 Canadian bird species and 13 amphibians.

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• Nature Tech for Biodiversity Sector Map launched!

Conservation International is proud to announce the launch of the Nature Tech for Biodiversity Sector Map, developed in partnership with the Nature Tech Collective! 

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• New WILDLABS Funding & Finance group

WildLabs will soon launch a 'Funding and Finance' group. What would be your wish list for such a group? Would you be interested in co-managing or otherwise helping out?

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• Maps, Education and Community

geocarto

• Latest Resource
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• Nature Tech for Biodiversity Sector Map launched!

Conservation International is proud to announce the launch of the Nature Tech for Biodiversity Sector Map, developed in partnership with the Nature Tech Collective! 

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• What Are Innovative Technologies, and Why Should Conservationists Care?

Conservationists use tools like drones, satellites, and camera traps to monitor ecosystems and scale their impact. But new challenges like transparency, funding gaps, and engagement remain. Web 3.0 technologies offer solutions, but adoption can be complex. Understanding their benefits and barriers is key.