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20172023
most citedTechnology Readiness Levels for Machine Learning Systems

189 citations · 243 across the 21 of their papers we have counts for

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Showing 2022Show all

7 papers · 1 filter

cs.CV2022★ 1 cited

Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning

Tarun Narayanan, Ajay Krishnan, Anirudh Koul +1

Applying Machine learning to domains like Earth Sciences is impeded by the lack of labeled data, despite a large corpus of raw data available in such domains. For instance, trainin…

cs.CV2022

Deep learning based landslide density estimation on SAR data for rapid response

Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5

This work aims to produce landslide density estimates using Synthetic Aperture Radar (SAR) satellite imageries to prioritise emergency resources for rapid response. We use the Unit…

cs.CV2022

SAR-based landslide classification pretraining leads to better segmentation

Vanessa Böhm, Wei Ji Leong, Ragini Bal Mahesh +5

Rapid assessment after a natural disaster is key for prioritizing emergency resources. In the case of landslides, rapid assessment involves determining the extent of the area affec…

eess.SP2022★ 5 cited

Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes

Vanessa Boehm, Wei Ji Leong, Ragini Bal Mahesh +5

With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency response…

physics.space-ph2022★ 15 cited

Global geomagnetic perturbation forecasting using Deep Learning

Vishal Upendran, Panagiotis Tigas, Banafsheh Ferdousi +6

Geomagnetically Induced Currents (GICs) arise from spatio-temporal changes to Earth's magnetic field which arise from the interaction of the solar wind with Earth's magnetosphere,…

cs.RO2022★ 6 cited

Learn-to-Race Challenge 2022: Benchmarking Safe Learning and Cross-domain Generalisation in Autonomous Racing

Jonathan Francis, Bingqing Chen, Siddha Ganju +10

We present the results of our autonomous racing virtual challenge, based on the newly-released Learn-to-Race (L2R) simulation framework, which seeks to encourage interdisciplinary…

cs.CV2022

CELESTIAL: Classification Enabled via Labelless Embeddings with Self-supervised Telescope Image Analysis Learning

Suhas Kotha, Anirudh Koul, Siddha Ganju +1

A common class of problems in remote sensing is scene classification, a fundamentally important task for natural hazards identification, geographic image retrieval, and environment…