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20212025
most citedThere Are No Data Like More Data- Datasets for Deep Learning in Earth Observation

47 citations · 48 across the 5 of their papers we have counts for

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5 papers

eess.IV2025

Adversarial Robustness of Deep Learning Models for Inland Water Body Segmentation from SAR Images

Siddharth Kothari, Srinivasan Murali, Sankalp Kothari +2

Inland water body segmentation from Synthetic Aperture Radar (SAR) images is an important task needed for several applications, such as flood mapping. While SAR sensors capture dat…

eess.IV2024

LapGSR: Laplacian Reconstructive Network for Guided Thermal Super-Resolution

Aditya Kasliwal, Ishaan Gakhar, Aryan Kamani +2

In the last few years, the fusion of multi-modal data has been widely studied for various applications such as robotics, gesture recognition, and autonomous navigation. Indeed, hig…

cs.CV202347 cited

There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation

Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu +4

Carefully curated and annotated datasets are the foundation of machine learning, with particularly data-hungry deep neural networks forming the core of what is often called Artific…

cs.CV20221 cited

Enhanced Vehicle Re-identification for ITS: A Feature Fusion approach using Deep Learning

Ashutosh Holla B, Manohara Pai M. M, Ujjwal Verma +1

In recent years, the development of robust Intelligent transportation systems (ITS) is tackled across the globe to provide better traffic efficiency by reducing frequent traffic pr…

cs.LG2021

Evaluating Predictive Uncertainty and Robustness to Distributional Shift Using Real World Data

Kumud Lakara, Akshat Bhandari, Pratinav Seth +1

Most machine learning models operate under the assumption that the training, testing and deployment data is independent and identically distributed (i.i.d.). This assumption doesn'…