Species distribution modeling for machine learning practitioners: A review
Conservation science depends on an accurate understanding of what's happening in a
given ecosystem. How many species live there? What is the makeup of the population? How …
given ecosystem. How many species live there? What is the makeup of the population? How …
When does contrastive visual representation learning work?
Recent self-supervised representation learning techniques have largely closed the gap
between supervised and unsupervised learning on ImageNet classification. While the …
between supervised and unsupervised learning on ImageNet classification. While the …
Geo-bench: Toward foundation models for earth monitoring
Recent progress in self-supervision has shown that pre-training large neural networks on
vast amounts of unsupervised data can lead to substantial increases in generalization to …
vast amounts of unsupervised data can lead to substantial increases in generalization to …
Satbird: a dataset for bird species distribution modeling using remote sensing and citizen science data
Biodiversity is declining at an unprecedented rate, impacting ecosystem services necessary
to ensure food, water, and human health and well-being. Understanding the distribution of …
to ensure food, water, and human health and well-being. Understanding the distribution of …
Overview of lifeclef 2022: an evaluation of machine-learning based species identification and species distribution prediction
Building accurate knowledge of the identity, the geographic distribution and the evolution of
species is essential for the sustainable development of humanity, as well as for biodiversity …
species is essential for the sustainable development of humanity, as well as for biodiversity …
Spatial implicit neural representations for global-scale species mapping
Estimating the geographical range of a species from sparse observations is a challenging
and important geospatial prediction problem. Given a set of locations where a species has …
and important geospatial prediction problem. Given a set of locations where a species has …
Overview of lifeclef 2020: a system-oriented evaluation of automated species identification and species distribution prediction
Building accurate knowledge of the identity, the geographic distribution and the evolution of
species is essential for the sustainable development of humanity, as well as for biodiversity …
species is essential for the sustainable development of humanity, as well as for biodiversity …
Deep learning models map rapid plant species changes from citizen science and remote sensing data
Anthropogenic habitat destruction and climate change are reshaping the geographic
distribution of plants worldwide. However, we are still unable to map species shifts at high …
distribution of plants worldwide. However, we are still unable to map species shifts at high …
[PDF][PDF] Overview of GeoLifeCLEF 2022: Predicting Species Presence from Multi-modal Remote Sensing, Bioclimatic and Pedologic Data.
Understanding the geographic distribution of species is a key concern in conservation. By
pairing species occurrences with environmental features, researchers can model the …
pairing species occurrences with environmental features, researchers can model the …
Very high resolution species distribution modeling based on remote sensing imagery: how to capture fine-grained and large-scale vegetation ecology with …
Species Distribution Models (SDMs) are fundamental tools in ecology for predicting the
geographic distribution of species based on environmental data. They are also very useful …
geographic distribution of species based on environmental data. They are also very useful …