Project SPARROW and the Future of Conservation Technology
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arXiv
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| Format: | Preprint |
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2026
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| author | Ferres, Juan M. Lavista Chalmers, Carl Segundo, Bruno Demuro Miao, Zhongqi Celis, Andres Hernandez Torres, Federico Alves Silva, Isai Daniel Chacon Roman, Anthony Cintron Kim, Allen Machado, Meygha Marotti, Luana Michaels, Amy Lopez, Daniela Ruiz Romero, Catherine Dodhia, Rahul Becker-Reshef, Inbal Arbelaez, Pablo |
| author_facet | Ferres, Juan M. Lavista Chalmers, Carl Segundo, Bruno Demuro Miao, Zhongqi Celis, Andres Hernandez Torres, Federico Alves Silva, Isai Daniel Chacon Roman, Anthony Cintron Kim, Allen Machado, Meygha Marotti, Luana Michaels, Amy Lopez, Daniela Ruiz Romero, Catherine Dodhia, Rahul Becker-Reshef, Inbal Arbelaez, Pablo |
| contents | Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessibility. We present SPARROW, a hardware and software open-source platform that integrates solar energy, edge artificial intelligence, and satellite communication to enable continuous, autonomous biodiversity monitoring in remote environments. Each SPARROW node combines a low-power Graphics Processing Unit (GPU) with modular visual, acoustic, and environmental sensors, performing on-device deep learning inference and transmitting summarized results through Low-Earth-Orbit (LEO) satellite or Global System for Mobile Communications (GSM) networks.
We deployed SPARROW across tropical, temperate, and montane ecosystems in Colombia, Peru, Tanzania, and the United States, where it sustained 24/7 operation under variable environmental conditions and collected more than two million images and acoustic recordings in the first 190 days. The system demonstrated robust real-time classification and adaptive power management, achieving full autonomy without on-site human intervention. By integrating renewable energy, on-edge AI, and open-source design, SPARROW lowers the technical and financial barriers to ecological monitoring and establishes a scalable foundation for a distributed, intelligent network of sensors, an emerging "Internet of Living Things" for planetary biodiversity monitoring. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_00108 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Project SPARROW and the Future of Conservation Technology Ferres, Juan M. Lavista Chalmers, Carl Segundo, Bruno Demuro Miao, Zhongqi Celis, Andres Hernandez Torres, Federico Alves Silva, Isai Daniel Chacon Roman, Anthony Cintron Kim, Allen Machado, Meygha Marotti, Luana Michaels, Amy Lopez, Daniela Ruiz Romero, Catherine Dodhia, Rahul Becker-Reshef, Inbal Arbelaez, Pablo Signal Processing Artificial Intelligence Global biodiversity is declining at unprecedented rates, yet the tools available to monitor and protect ecosystems remain limited by constraints in power, connectivity, and accessibility. We present SPARROW, a hardware and software open-source platform that integrates solar energy, edge artificial intelligence, and satellite communication to enable continuous, autonomous biodiversity monitoring in remote environments. Each SPARROW node combines a low-power Graphics Processing Unit (GPU) with modular visual, acoustic, and environmental sensors, performing on-device deep learning inference and transmitting summarized results through Low-Earth-Orbit (LEO) satellite or Global System for Mobile Communications (GSM) networks. We deployed SPARROW across tropical, temperate, and montane ecosystems in Colombia, Peru, Tanzania, and the United States, where it sustained 24/7 operation under variable environmental conditions and collected more than two million images and acoustic recordings in the first 190 days. The system demonstrated robust real-time classification and adaptive power management, achieving full autonomy without on-site human intervention. By integrating renewable energy, on-edge AI, and open-source design, SPARROW lowers the technical and financial barriers to ecological monitoring and establishes a scalable foundation for a distributed, intelligent network of sensors, an emerging "Internet of Living Things" for planetary biodiversity monitoring. |
| title | Project SPARROW and the Future of Conservation Technology |
| topic | Signal Processing Artificial Intelligence |
| url | https://arxiv.org/abs/2606.00108 |