International Journal of Machine Learning, AI & Data Science Evolution

E-ISSN: 3067-5073

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Call for Paper Volume 2 Issue 9 September 2026 Submit your research before last 3 days of this month to publish your research paper in the issue of September.
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Volume 2 Issue 9
September 2026

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AI-Driven Urban Biodiversity Mapping: Integrating Geospatial Intelligence, Ecology and Citizen Science

Author(s) Xiangzhong Luo
Country Singapore
Abstract Urban areas contain heterogeneous ecological environments, including public parks, wetlands, gardens, vacant land, street trees, transport corridors, waterways, green roofs, institutional campuses, and small informal green spaces. These habitats can support diverse plant and animal communities, but their ecological value is often insufficiently represented in conventional land-use inventories. Field surveys remain essential for reliable biodiversity assessment, yet they can be expensive, spatially restricted, and difficult to repeat frequently across an entire city. Artificial intelligence and geospatial technologies provide opportunities to extend ecological monitoring by integrating satellite imagery, aerial data, environmental sensors, species observations, acoustic recordings, photographs, and citizen-science contributions.This paper examines an integrated framework for AI-driven urban biodiversity mapping. The study combines a conceptual literature review with an illustrative simulation assessing how complementary data layers may expand validated species-record coverage. In the simulation, a normalized validated species-record index increases from 45 for professional field surveys alone to 94 after citizen observations, satellite data, acoustic and image sensors, and AI-assisted validation are incorporated. These values are methodological illustrations and do not represent field-survey findings. The analysis demonstrates that AI can support land-cover classification, species identification, habitat-suitability modelling, ecological-corridor assessment, and identification of under-surveyed locations.
The paper also identifies substantial challenges involving uneven observation effort, geographic bias, species misidentification, limited detection of inconspicuous organisms, remote-sensing resolution, algorithmic uncertainty, privacy, and the potential exclusion of communities with limited digital access. Citizen observations should therefore be treated as valuable but nonuniform ecological evidence requiring validation and bias correction. Responsible urban biodiversity mapping should combine automated analysis with professional field verification, transparent uncertainty estimates, reproducible data standards, and meaningful community participation. Such integration can support more spatially equitable conservation planning and improve understanding of biodiversity within rapidly changing urban environments.
Keywords artificial intelligence; urban biodiversity; geospatial intelligence; citizen science; remote sensing; species-distribution modelling; ecological planning; green infrastructure
Field Engineering
Published In Volume 2, Issue 9, September 2026
Published On 2026-09-04
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E-ISSN: 3067-5073
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