Srikumar Sastry

I am a PhD candidate at Washington University in Imaging Science, working in Multimodal Vision Research Laboratory led by Dr. Nathan Jacobs.

I have an MS in Geoinformatics from The Faculty ITC, Geoinformation Science and Earth Observation. During my Masters, I was supervised by Dr. Mariana Belgiu and Dr. Raian Vargas Maretto, and worked on developing active learning methods in remote sensing.

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Research

I am interested in computer vision, remote sensing and generative/bayesian modeling. My work involves fusing heterogeneous multimodal data for remote sensing applications involving representation learning, satellite image synthesis, species distribution modeling, change detection, and land cover mapping.

A soundscape is defined by the acoustic environment a person perceives at a location. In this work, we propose a framework for mapping soundscapes across the Earth.

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.

We consider the task of active geo-localization (AGL) in which an agent uses a sequence of visual cues observed during aerial navigation to find a target specified through multiple possible modalities.

We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control. The global style control is via textual prompts or geographic location. These enable the specification of scene semantics or regional appearance respectively, and can be used together.

We propose a weakly supervised approach for creating maps using free-form textual descriptions. We refer to this work of creating textual maps as zero-shot mapping.

We propose a novel model called Vision-Language Pseudo-Labeling (VLPL) which uses a vision-language model to suggest strong positive and negative pseudo-labels.

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 metadata-aware self-supervised learning (SSL) framework useful for fine-grained classification and ecological mapping of bird species around the world. Our framework unifies two SSL strategies: Contrastive Learning (CL) and Masked Image Modeling (MIM), while also enriching the embedding space with metadata available with ground-level imagery of birds.

We focus on the task of soundscape mapping, which involves predicting the most probable sounds that could be perceived at a particular geographic location. We utilise recent state-of-the-art models to encode geotagged audio, a textual description of the audio, and an overhead image of its capture location using contrastive pre-training.

We consider the problem of cost effective active learning for semantic segmentation, which aims at reducing the efforts of semantically annotating images.

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Awesome Huggingface Demos