GeoPl@ntNet: A Platform for Exploring Essential Biodiversity Variables
This paper describes GeoPl@ntNet, an interactive web application designed to make Essential Biodiversity Variables accessible and understandable to everyone through dynamic maps and fact sheets. Its core purpose is to allow users to explore high-resolution AI-generated maps of species distributions, habitat types, and biodiversity indicators across Europe. These maps, developed through a cascading pipeline involving convolutional neural networks and large language models, provide an intuitive yet information-rich interface to better understand biodiversity, with resolutions as precise as 50x50 meters. The website also enables exploration of specific regions, allowing users to select areas of interest on the map (e.g., urban green spaces, protected areas, or riverbanks) to view local species and their coverage. Additionally, GeoPl@ntNet generates comprehensive reports for selected regions, including insights into the number of protected species, invasive species, and endemic species.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Jellyfish Species Identification: A CNN Based Artificial Neural Network Approach
Jellyfish, a diverse group of gelatinous marine organisms, play a crucial role in maintaining marine ecosystems but pose significant challenges for biodiversity and conservation due to their rapid proliferation and ecolo…
Eyes on the Grass: Biodiversity-Increasing Robotic Mowing Using Deep Visual Embeddings
This paper presents a robotic mowing framework that actively enhances garden biodiversity through visual perception and adaptive decision-making. Unlike passive rewilding approaches, the proposed system uses deep feature…
Exploring the potential of collaborative UAV 3D mapping in Kenyan savanna for wildlife research
UAV-based biodiversity conservation applications have exhibited many data acquisition advantages for researchers. UAV platforms with embedded data processing hardware can support conservation challenges through 3D habita…
Simultaneous Localization and MappingAutonomous AI Bird Feeder for Backyard Biodiversity Monitoring
This paper presents a low cost, on premise system for autonomous backyard bird monitoring in Belgian urban gardens. A motion triggered IP camera uploads short clips via FTP to a local server, where frames are sampled and…
DEEP-SEA: Deep-Learning Enhancement for Environmental Perception in Submerged Aquatics
Continuous and reliable underwater monitoring is essential for assessing marine biodiversity, detecting ecological changes and supporting autonomous exploration in aquatic environments. Underwater monitoring platforms re…
Underwater Image Restoration