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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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• 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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• Behind the Buzz Season 1: From Data to Decisions

Come along to the first season of Behind the Buzz, where we’ll bring in experts to break down the basics of global conservation policy frameworks through the lens of animal movement and explore how our community can better translate movement data to decision-making.

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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.

• 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! 

• Latest Resource
•  / 
• 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! 

• Latest Resource
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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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• FROM VULNERABILITY TO SECURITY: INSPIRING COEXISTENCE THROUGH A RESILIENT BOMA

This project aimed to reduce human-carnivore conflict by building a resilient boma for a vulnerable family in Hunyari village. It served as a model for other families, promoting stronger livestock protection. The initiative included community engagement on predator deterrence and sustainable building practices. The success of the pilot is inspiring others to adopt similar solutions.  

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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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• 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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• 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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• Maps, Education and Community

geocarto

• 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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• Brokering finance deals to fund nature restoration with Rob Gardner

This is the title of an episode of Ben Goldsmith's 'Rewilding the World' podcast series. I have been wondering for a while, how is it done? Here is a crystal clear outline.

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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! 

• Latest Resource
•  / 
• 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! 

• Latest Resource
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• INSTANT DETECT 2.0 - ALPHA TESTING

The worst thing a new conservation technology can do is become another maintenance burden on already stretched field teams. This meant Instant Detect 2.0 had to work perfectly from day 1. In this update, Sam Seccombe describes the extensive Alpha testing conducted at Whipsnade Zoo to validate the performance and find the limits of ZSL's Instant Detect 2.0 system.

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• MyProgress: Advancing GIS and R programming skills.

A fascinating journey of unleashing my potentials in spatial analysis using ArcGIS Pro and R programming.