no code implementations • 6 Dec 2023 • Patrick Beukema, Favyen Bastani, Piper Wolters, Henry Herzog, Joe Ferdinando
Illegal, unreported, and unregulated (IUU) fishing poses a global threat to ocean habitats.
1 code implementation • 29 Nov 2023 • Piper Wolters, Favyen Bastani, Aniruddha Kembhavi
Super-Resolution for remote sensing has the potential for huge impact on planet monitoring by producing accurate and realistic high resolution imagery on a frequent basis and a global scale.
1 code implementation • ICCV 2023 • Favyen Bastani, Piper Wolters, Ritwik Gupta, Joe Ferdinando, Aniruddha Kembhavi
Remote sensing images are useful for a wide variety of planet monitoring applications, from tracking deforestation to tackling illegal fishing.
1 code implementation • NeurIPS 2021 • Favyen Bastani, Songtao He, Sam Madden
In this paper, we propose a self-supervised learning procedure for training a robust multi-object tracking (MOT) model given only unlabeled video.
no code implementations • 13 Oct 2021 • Favyen Bastani, Songtao He, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi
To address this challenge, much work has studied automatically processing geospatial data sources such as GPS trajectories and satellite images to reduce the cost of maintaining digital maps.
1 code implementation • ICCV 2021 • Favyen Bastani, Sam Madden
The increasing availability of satellite and aerial imagery has sparked substantial interest in automatically updating street maps by processing aerial images.
no code implementations • NeurIPS Workshop AIPLANS 2021 • Stephen Mell, Favyen Bastani, Stephan Zdancewic, Osbert Bastani
A key challenge is that queries are difficult for end users to develop: queries must reason about complex spatial and temporal patterns in object trajectories in order to select trajectories of interest, and predicates often include real-valued parameters (e. g., whether two cars are within a certain distance) that can be tedious to manually tune.
no code implementations • 24 Mar 2021 • Songtao He, Favyen Bastani, Mohammad Alizadeh, Hari Balakrishnan, Michael Cafarella, Tim Kraska, Sam Madden
We show TagMe can produce high-quality object annotations in a fully-automatic and low-cost way.
1 code implementation • ECCV 2020 • Songtao He, Favyen Bastani, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Mohamed M. Elshrif, Samuel Madden, Amin Sadeghi
Inferring road graphs from satellite imagery is a challenging computer vision task.
1 code implementation • 28 Dec 2019 • Songtao He, Favyen Bastani, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Samuel Madden, Mohammad Amin Sadeghi
The usage of graph neural networks allows information propagation on the road network graph and eliminates the receptive field limitation of image classifiers.
no code implementations • 2 Oct 2019 • Favyen Bastani, Songtao He, Satvat Jagwani, Edward Park, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, Mohammad Amin Sadeghi
Through an evaluation on a large-scale dataset including satellite imagery, GPS trajectories, and ground-truth map data in forty cities, we show that Mapster makes automation practical for map editing, and enables the curation of map datasets that are more complete and up-to-date at less cost.
no code implementations • 17 Jun 2019 • Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden
Systems to automatically infer road network graphs from aerial imagery and GPS trajectories have been proposed to improve coverage of road maps.
1 code implementation • CVPR 2018 • Favyen Bastani, Songtao He, Sofiane Abbar, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Sam Madden, David DeWitt
Mapping road networks is currently both expensive and labor-intensive.