GeoPl@ntNet: A Platform for Exploring Essential Biodiversity Variables

• URL: http://arxiv.org/abs/2511.13790v1
• Date: Sun, 16 Nov 2025 17:36:44 GMT
• Title: GeoPl@ntNet: A Platform for Exploring Essential Biodiversity Variables
• Authors: Lukas Picek, César Leblanc, Alexis Joly, Pierre Bonnet, Rémi Palard, Maximilien Servajean,
• Abstract summary: GeoPl@ntNet is an interactive web application designed to make Essential Biodiversity Variables accessible to everyone.<n>It allows users to explore high-resolution AI-generated maps of species distributions, habitat types, and biodiversity indicators across Europe.<n>It also generates comprehensive reports for selected regions, including insights into the number of protected species, invasive species, and endemic species.
• Score: 5.59149001845973
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• CrypticBio: A Large Multimodal Dataset for Visually Confusing Biodiversity [3.73232466691291]
  We present CrypticBio, the largest publicly available dataset of visually confusing species.<n>Criticized from real-world trends in species misidentification among community annotators of iNaturalist, CrypticBio contains 52K unique cryptic groups spanning 67K species.
  arXiv  Detail & Related papers  (2025-05-16T14:35:56Z)
• BioCube: A Multimodal Dataset for Biodiversity Research [0.31498833540989407]
  We introduce BioCube, a fine-grained global dataset for ecology and biodiversity research.<n>BioCube incorporates species observations through images, audio recordings and descriptions, environmental DNA, vegetation indices, agricultural, forest, land indicators, and high-resolution climate variables.
  arXiv  Detail & Related papers  (2025-05-16T09:46:08Z)
• Mapping biodiversity at very-high resolution in Europe [2.4081658738294283]
  This paper describes a cascading multimodal pipeline for high-resolution biodiversity mapping across Europe.<n>The proposed pipeline first predicts species compositions using a deep-SDM, a multimodal model trained on remote sensing, climate time series, and species occurrence data at 50x50m resolution.<n>These predictions are then used to generate biodiversity indicator maps and classify habitats with Pl@ntBERT.
  arXiv  Detail & Related papers  (2025-04-07T16:15:52Z)
• Feedforward Few-shot Species Range Estimation [61.60698161072356]
  Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts.<n> accurate range estimates are only available for a relatively small proportion of all known species.<n>We outline a new approach for few-shot species range estimation to address the challenge of accurately estimating the range of a species from limited data.
  arXiv  Detail & Related papers  (2025-02-20T19:13:29Z)
• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia.<n>Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions.<n>Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• MapGPT: Map-Guided Prompting with Adaptive Path Planning for Vision-and-Language Navigation [73.81268591484198]
  Embodied agents equipped with GPT have exhibited extraordinary decision-making and generalization abilities across various tasks. We present a novel map-guided GPT-based agent, dubbed MapGPT, which introduces an online linguistic-formed map to encourage global exploration. Benefiting from this design, we propose an adaptive planning mechanism to assist the agent in performing multi-step path planning based on a map.
  arXiv  Detail & Related papers  (2024-01-14T15:34:48Z)
• SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data [68.2366021016172]
  We present SatBird, a satellite dataset of locations in the USA with labels derived from presence-absence observation data from the citizen science database eBird. We also provide a dataset in Kenya representing low-data regimes. We benchmark a set of baselines on our dataset, including SOTA models for remote sensing tasks.
  arXiv  Detail & Related papers  (2023-11-02T02:00:27Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• Bird Distribution Modelling using Remote Sensing and Citizen Science data [31.375576105932442]
  Climate change is a major driver of biodiversity loss. There are significant knowledge gaps about the distribution of species. We propose an approach leveraging computer vision to improve species distribution modelling.
  arXiv  Detail & Related papers  (2023-05-01T20:27:11Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.