The GeoLifeCLEF 2020 Dataset
- URL: http://arxiv.org/abs/2004.04192v1
- Date: Wed, 8 Apr 2020 18:30:00 GMT
- Title: The GeoLifeCLEF 2020 Dataset
- Authors: Elijah Cole, Benjamin Deneu, Titouan Lorieul, Maximilien Servajean,
Christophe Botella, Dan Morris, Nebojsa Jojic, Pierre Bonnet, Alexis Joly
- Abstract summary: We present the GeoLifeCLEF 2020 dataset, which consists of 1.9 million species observations paired with high-resolution remote sensing imagery, land cover data, and altitude.
We also discuss the GeoLifeCLEF 2020 competition, which aims to use this dataset to advance the state-of-the-art in location-based species recommendation.
- Score: 13.274586385114622
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Understanding the geographic distribution of species is a key concern in
conservation. By pairing species occurrences with environmental features,
researchers can model the relationship between an environment and the species
which may be found there. To facilitate research in this area, we present the
GeoLifeCLEF 2020 dataset, which consists of 1.9 million species observations
paired with high-resolution remote sensing imagery, land cover data, and
altitude, in addition to traditional low-resolution climate and soil variables.
We also discuss the GeoLifeCLEF 2020 competition, which aims to use this
dataset to advance the state-of-the-art in location-based species
recommendation.
Related papers
- A Geospatial Approach to Predicting Desert Locust Breeding Grounds in Africa [3.6826233660285395]
locust swarms present a major threat to agriculture and food security.
Our study develops an operationally-ready model for predicting locust breeding grounds.
arXiv Detail & Related papers (2024-03-11T16:13:58Z) - 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) - GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE.
We collect data from open-released geographic resources and introduce six natural language understanding tasks.
We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
arXiv Detail & Related papers (2023-05-11T03:21:56Z) - 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) - Spatiotemporal modeling of European paleoclimate using doubly sparse
Gaussian processes [61.31361524229248]
We build on recent scale sparsetemporal GPs to reduce the computational burden.
We successfully employ such a doubly sparse GP to construct a probabilistic model of paleoclimate.
arXiv Detail & Related papers (2022-11-15T14:15:04Z) - Activation Regression for Continuous Domain Generalization with
Applications to Crop Classification [48.795866501365694]
Geographic variance in satellite imagery impacts the ability of machine learning models to generalise to new regions.
We model geographic generalisation in medium resolution Landsat-8 satellite imagery as a continuous domain adaptation problem.
We develop a dataset spatially distributed across the entire continental United States.
arXiv Detail & Related papers (2022-04-14T15:41:39Z) - Linking Sap Flow Measurements with Earth Observations [0.0]
We have tested the suitability of earth observations for modelling canopy transpiration.
Within a machine learning framework, we have tested the suitability of earth observations for modelling canopy transpiration.
arXiv Detail & Related papers (2021-08-03T04:40:15Z)
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.