Adeel Ahmad

Aayush Dhakal, Srikumar Sastry, Subash Khanal et al.

The choice of representation for geographic location significantly impacts the accuracy of models for a broad range of geospatial tasks, including fine-grained species classification, population density estimation, and biome classification. Recent works like SatCLIP and GeoCLIP learn such representations by contrastively aligning geolocation with co-located images. While these methods work exceptionally well, in this paper, we posit that the current training strategies fail to fully capture the important visual features. We provide an information-theoretic perspective on why the resulting embeddings from these methods discard crucial visual information that is important for many downstream tasks. To solve this problem, we propose a novel retrieval-augmented strategy called RANGE. We build our method on the intuition that the visual features of a location can be estimated by combining the visual features from multiple similar-looking locations. We evaluate our method across a wide variety of tasks. Our results show that RANGE outperforms the existing state-of-the-art models with significant margins in most tasks. We show gains of up to 13.1% on classification tasks and 0.145 $R^2$ on regression tasks. All our code and models will be made available at: https://github.com/mvrl/RANGE.

Aayush Dhakal, Subash Khanal, Srikumar Sastry et al.

In remote sensing, we are interested in modeling various modalities for some geographic location. Several works have focused on learning the relationship between a location and type of landscape, habitability, audio, textual descriptions, etc. Recently, a common way to approach these problems is to train a deep-learning model that uses satellite images to infer some unique characteristics of the location. 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. To do this, we use satellite images as the binding element and contrastively align all other modalities to the satellite image data. Our training results in a joint embedding space with multiple types of data: satellite image, ground-level image, audio, and text. Furthermore, our approach does not require a single complex dataset that contains all the modalities mentioned above. Rather it only requires multiple satellite-image paired data. While we only align three modalities in this paper, we present a general framework that can be used to create an embedding space with any number of modalities by using satellite images as the binding element. Our results show that, unlike traditional unimodal models, GeoBind is versatile and can reason about multiple modalities for a given satellite image input.