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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/Should UAVs Be The Answer To Urbanisation & Wildlife Conflicts  

Over the years, a large number of developments have gone up in the area that I live in and the municipality is not doing what they should when it comes to upholding the laws that have been put in place to protect the wildlife in this protected paradise. Is  anyone else around the world facing this in their communities? Can/should drones and open source software be the answer to better monitoring of construction companies that are in breach of wildlife regulations? I would like to get others thoughts on this subject as it can be complicated. GeoWing Academy has been using drones and opensource software to monitor some of the changes and damages to the environment to illustrate the issues to the local municipality and government. There is a PDF version of the article attached should the images in the article not be visible

• Latest Resource
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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.

• Latest Resource
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• Fires in the Serengeti: Burn Severity & Remote Sensing with Earth Engine

Fires in Serengeti and Masai Mara National Parks have burned massive areas this year. With Google Earth Engine, it's possible to quantify burn severity using the normalized burn ratio function, then calculate the total area burned by classification areas.

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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

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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.

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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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• Advancing Hierarchical Classification of Ocean Life

Join the FathomNet Kaggle Competition to show us how you would develop a model that can accurately classify varying taxonomic ranks!

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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.