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Nestled in the heart of the Yucatán Peninsula, the Dzilam State Reserve is a biodiversity hotspot. Enter Tech4Nature Mexico, the result of a collaboration between tech experts, conservationists, and local communities. This project is all about using AI for good – monitoring, understanding, and safeguarding biodiversity like never before.
Tech4Nature Mexico showcases a unique blend of community-driven methodologies, machine learning techniques, and multi-sector partnerships. This project underscores the significance of responsible and ethical development of advanced technologies, ensuring proactive mitigation of social and environmental risks.
Access the webinar The Road to Tech4Nature Mexico Phase 2 to meet the people and learn the stories behind the project, and don't miss our newly launched report which not only presents the methodology and achievements of this initiative, but also the opportunities and strategies to scale and replicate initiatives like these that leverage AI systems for conservation.
Tech4Nature Mexico aims to accelerate the effective conservation and regeneration of biodiversity and ecosystem health by strengthening the monitoring, conservation, and understanding of the effects of climate change on priority ecosystems and species in the mangrove zone of the Yucatan Peninsula.
This is achieved by employing the power of community-centered approaches, AI systems, and multi-sectoral collaborations in the Dzilam de Bravo reserve.
Regional CNNs for species detection and identification
AI Ethics Protocols
Data-informed conservation actions
Our mission at Tech4Nature centers on understanding, preserving and restoring the Dzilam State Reserve to provide a secure sanctuary for a rich variety of plant and animal species.
Nestled in the northeastern region of Yucatan, the Dzilam State Reserve is a natural protected area with over 69,000 hectares that belongs to the municipalities of Dzilam de Bravo and San Felipe.
This reserve holds a special status as a critical wetland conservation site, boasting nearly 290 species of fauna intricately linked with over 300 flora species. It spans five distinct vegetation types, including coastal dunes, mangroves, petenes, along with vibrant aquatic flora in coastal lagoons.
Analyzed with the
ModelArts AI platform from Huawei Cloud
Analyzed with the
Arbimon AI platform, from Rainforest Connection
+120 SPECIES IDENTIFIED
These species are part of a Convolutional Neural Network (CNN) for automatic identification
Automatically identified and validated by experts
The monitoring algorithms provided valuable information that validated the presence of threatened species in the Reserve in a way never before verified.
The data provided tools to strengthen knowledge of the biodiversity in the area and local involvement to reinforce conservation.
Forged a powerful alliance between the automatic jaguar identification model and esteemed biologists specializing in jaguar studies.
Human threats are and will continue to be reduced thanks to the continuous monitoring and presence of authorities in the area, strengthening the protection of jaguars in the area.
Local partners and the local community have been trained in the use of these technologies for the protection of nature.
The information generated is supporting the process of including the Reserve in the IUCN Green List of Protected and Conserved Areas.
Inputs for the replicability of AI tools and resources for the protection of biodiversity for Latin America were generated.
Creation of recommendations for good practices in the ethical use of AI for conservation.
Pilot positioning as an international case study.
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