Bird Distribution Modelling using Remote Sensing and Citizen Science data

• URL: http://arxiv.org/abs/2305.01079v1
• Date: Mon, 1 May 2023 20:27:11 GMT
• Title: Bird Distribution Modelling using Remote Sensing and Citizen Science data
• Authors: M\'elisande Teng, Amna Elmustafa, Benjamin Akera, Hugo Larochelle, David Rolnick
• Abstract summary: 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.
• Score: 31.375576105932442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Climate change is a major driver of biodiversity loss, changing the geographic range and abundance of many species. However, there remain significant knowledge gaps about the distribution of species, due principally to the amount of effort and expertise required for traditional field monitoring. We propose an approach leveraging computer vision to improve species distribution modelling, combining the wide availability of remote sensing data with sparse on-ground citizen science data. We introduce a novel task and dataset for mapping US bird species to their habitats by predicting species encounter rates from satellite images, along with baseline models which demonstrate the power of our approach. Our methods open up possibilities for scalably modelling ecosystems properties worldwide.

Related papers

• 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. Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions. 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)
• GeoPlant: Spatial Plant Species Prediction Dataset [4.817737198128259]
  Species Distribution Models (SDMs) predict species across space from spatially explicit features. We have designed and developed a new European-scale dataset for SDMs at high spatial resolution (10-50 m) The dataset comprises 5M heterogeneous Presence-Only records and 90k exhaustive Presence-Absence survey records.
  arXiv  Detail & Related papers  (2024-08-25T20:09:46Z)
• Predicting Species Occurrence Patterns from Partial Observations [21.009271008147785]
  We introduce the problem of predicting species occurrence patterns given (a) satellite imagery, and (b) known information on the occurrence of other species. To evaluate algorithms on this task, we introduce SatButterfly, a dataset of satellite images, environmental data and observational data for butterflies. We propose a general model, R-Tran, for predicting species occurrence patterns that enables the use of partial observational data wherever found.
  arXiv  Detail & Related papers  (2024-03-26T18:29:39Z)
• Neural Plasticity-Inspired Multimodal Foundation Model for Earth Observation [48.66623377464203]
  Our novel approach introduces the Dynamic One-For-All (DOFA) model, leveraging the concept of neural plasticity in brain science. This dynamic hypernetwork, adjusting to different wavelengths, enables a single versatile Transformer jointly trained on data from five sensors to excel across 12 distinct Earth observation tasks.
  arXiv  Detail & Related papers  (2024-03-22T17:11:47Z)
• LD-SDM: Language-Driven Hierarchical Species Distribution Modeling [9.620416509546471]
  We focus on the problem of species distribution modeling using global-scale presence-only data. To capture a stronger implicit relationship between species, we encode the taxonomic hierarchy of species using a large language model. We propose a novel proximity-aware evaluation metric that enables evaluating species distribution models.
  arXiv  Detail & Related papers  (2023-12-13T18:11:37Z)
• 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)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)

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.