Search Results for author: Favyen Bastani

Found 13 papers, 7 papers with code

Zooming Out on Zooming In: Advancing Super-Resolution for Remote Sensing

1 code implementation29 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.


SatlasPretrain: A Large-Scale Dataset for Remote Sensing Image Understanding

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.

Time Series Time Series Analysis

Self-Supervised Multi-Object Tracking with Cross-Input Consistency

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.

Multi-Object Tracking Self-Supervised Learning

Updating Street Maps using Changes Detected in Satellite Imagery

no code implementations13 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.

Beyond Road Extraction: A Dataset for Map Update using Aerial Images

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.

Synthesizing Video Trajectory Queries

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.

Active Learning Object Tracking

RoadTagger: Robust Road Attribute Inference with Graph Neural Networks

1 code implementation28 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.


Inferring and Improving Street Maps with Data-Driven Automation

no code implementations2 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.

Machine-Assisted Map Editing

no code implementations17 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.

graph construction

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