no code implementations • 17 Apr 2024 • Aayush Dhakal, Subash Khanal, Srikumar Sastry, Adeel Ahmad, Nathan Jacobs
In this work, we present a deep-learning model, GeoBind, that can infer about multiple modalities, specifically text, image, and audio, from satellite imagery of a location.
1 code implementation • 9 Apr 2024 • Srikumar Sastry, Subash Khanal, Aayush Dhakal, Nathan Jacobs
We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control.
no code implementations • 13 Dec 2023 • Srikumar Sastry, Xin Xing, Aayush Dhakal, Subash Khanal, Adeel Ahmad, Nathan Jacobs
Further, we propose a novel proximity-aware evaluation metric that enables evaluating species distribution models using any pixel-level representation of ground-truth species range map.
1 code implementation • 29 Oct 2023 • Srikumar Sastry, Subash Khanal, Aayush Dhakal, Di Huang, Nathan Jacobs
We propose a metadata-aware self-supervised learning~(SSL)~framework useful for fine-grained classification and ecological mapping of bird species around the world.
1 code implementation • 19 Sep 2023 • Subash Khanal, Srikumar Sastry, Aayush Dhakal, Nathan Jacobs
We focus on the task of soundscape mapping, which involves predicting the most probable sounds that could be perceived at a particular geographic location.
Ranked #1 on Cross-Modal Retrieval on SoundingEarth (using extra training data)
no code implementations • 29 Jul 2023 • Aayush Dhakal, Adeel Ahmad, Subash Khanal, Srikumar Sastry, Hannah Kerner, Nathan Jacobs
For a given location and overhead image, our model predicts the expected CLIP embeddings of the ground-level scenery.
1 code implementation • 29 Jun 2022 • Subash Khanal, Benjamin Brodie, Xin Xing, Ai-Ling Lin, Nathan Jacobs
There have been several post-hoc explanation approaches developed to explain pre-trained black-box neural networks.