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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
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Posts
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Blog Post number 4
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portfolio
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publications
Spatial Implicit Neural Representations for Global-Scale Species Mapping
Published in Fortieth International Conference on Machine Learning, 2023
Here we investigate implicit neural representations for jointly estimating the spatial range of thousands of species from noisy, sparse, community collected data, with new benchmark tasks for geospatial representations and species distribution models.
Code
Demo
Website
Active Learning-Based Species Range Estimation
Published in Advances in Neural Information Processing Systems 37, 2023
We introduce a new active learning based approach for the prediction of a species’ range from limited data and evaluate our approach on two ground truth datasets. Our approach essentially combines species distribution models for well known species in order to create improved range maps for our target species, and suggests new locations to search for the target species to most improve our current range map.
Code
Generating Binary Species Range Maps
Published in Computer Vision for Ecology Workshop at The 18th European Conference on Computer Vision, 2024
Deep learning-based SDMs generate a continuous probability representing the presence of a species at a given location, which must be binarized by setting per-species thresholds to obtain binary range maps. However, selecting appropriate per-species thresholds to binarize these predictions is non-trivial, since different species can require different thresholds. In this work, we evaluate different approaches for automatically identifying the best thresholds for binarizing range maps using presence-only data. This includes approaches that require the generation of additional pseudo-absence data, along with ones that only require presence data. We also propose an extension of an existing presence-only technique that is more robust to outliers. We perform a detailed evaluation of different thresholding techniques on the tasks of binary range estimation and large-scale fine-grained visual classification, and we demonstrate improved performance over existing approaches using our technique.
Code
Combining Observational Data and Language for Species Range Estimation
Published in Advances in Neural Information Processing Systems 38, 2024
We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia, covering habitat preferences and range descriptions for tens of thousands of species. Our framework maps locations, species, and text descriptions into a common space, facilitating the learning of rich spatial covariates at a global scale and enabling zero-shot range estimation from textual descriptions.
Code
Feedforward Few-shot Species Range Estimation
Published in Forty-Second International Conference on Machine Learning, 2025
Knowing where a particular species can or cannot be found on Earth is crucial for ecological research and conservation efforts. By mapping the spatial ranges of all species, we would obtain deeper insights into how global biodiversity is affected by climate change and habitat loss. However, accurate range estimates are only available for a relatively small proportion of all known species. For the majority of the remaining species, we typically only have a small number of records denoting the spatial locations where they have previously been observed. 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. During inference, our model takes a set of spatial locations as input, along with optional metadata such as text or an image, and outputs a species encoding that can be used to predict the range of a previously unseen species in a feedforward manner. We evaluate our approach on two challenging benchmarks, where we obtain state-of-the-art range estimation performance, in a fraction of the compute time, compared to recent alternative approaches.
Code
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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