publications

2025

1. WACV 2025

   HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction

   Shaurya Rathore, Aravind Shenoy, Krish Didwania, and 2 more authors

   In Proceedings of the Winter Conference on Applications of Computer Vision, 2025

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2. IEEE OJITS

   Vehicle Re-Identification and Tracking: Algorithmic Approach, Challenges and Future Directions

   Ashutosh Holla, Manohara Pai MM, Ujjwal Verma, and 1 more author

   IEEE Open Journal of Intelligent Transportation Systems, 2025

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3. Robust Fault Detection System for Batteries in Renewable Energy Storage Systems

   Chinmay Murlidhar Kadnur Rao, Yuvika Chaudhary, Nirbhay Singhal, and 1 more author

   In 2025 9th International Conference on Green Energy and Applications (ICGEA), 2025
4. IEEE GRSM

   Computer Vision for Earth Observation: The Third IEEE GRSS Image Analysis and Data Fusion School

   Silvia Ullo, Gemine Vivone, Gülşen Taşkin, and 4 more authors

   IEEE Geoscience and Remote Sensing Magazine (GRSM), 2025

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5. Scientific Reports

   Contextual information based anomaly detection for multi-scene aerial videos

   S Girisha, Ujjwal Verma, Manohara MM Pai, and 1 more author

   Scientific Reports, 2025

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6. IEEE GRSM

   2025 IEEE GRSS Data Fusion Contest: All-Weather Land Cover and Building Damage Mapping

   Claudio Persello, Saurabh Prasad, Ujjwal Verma, and 8 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2025

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7. ICCV 2025

   Fourier Domain Adaptation for Traffic Light Detection in Adverse Weather

   Ishaan Gakhar, Aryaman Gupta, Aryesh Guha, and 2 more authors

   In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2025

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2024

1. IEEE GRSM

   Computer Vision for Earth Observation: The Second GRSS Image Analysis and Data Fusion School [Technical Committees]

   Silvia Liberata Ullo, Gemine Vivone, Gülşen Taşkın, and 2 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2024

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2. Neurocomputing

   MSFFT: Multi-Scale Feature Fusion Transformer for cross platform vehicle re-identification

   Ashutosh Holla, Manohara Pai MM, Ujjwal Verma, and 1 more author

   Neurocomputing, 2024

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3. IEEE IGARSS

   Towards Interpretable Machine Learning Metrics For Earth Observation Image Analysis

   Ujjwal Verma, Dalton Lunga, and Abhishek Potnis

   In IGARSS 2024-2024 IEEE International Geoscience and Remote Sensing Symposium, 2024

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4. IEEE GRSM

   InGARSS 2023 in Bangalore: Striking a Balance [Conference Reports]

   Jaya Sreevalsan-Nair, Amruth Kiran, Avik Bhattacharya, and 5 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2024

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5. IEEE GRSL

   Recent advances in machine learning for remote sensing toward the sustainable development goals

   Ujjwal Verma, Dalton Lunga, Ronny Hänsch, and 2 more authors

   IEEE Geoscience and Remote Sensing Letters, 2024

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2023

1. IEEE Access

   Fish tracking and continual behavioral pattern clustering using novel Sillago Sihama Vid (SSVid)

   S Shreesha, MM Manohara Pai, Ujjwal Verma, and 1 more author

   IEEE Access, 2023

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2. IEEE Access

   Enhanced vehicle re-identification for smart city applications using zone specific surveillance

   B Ashutosh Holla, MM Manohara Pai, Ujjwal Verma, and 1 more author

   IEEE Access, 2023

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3. Neurocomputing

   Texture based prototypical network for few-shot semantic segmentation of forest cover: Generalizing for different geographical regions

   Gokul Puthumanaillam and Ujjwal Verma

   Neurocomputing, 2023

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4. IEEE GRSM

   Computer Vision for Earth Observation—The First IEEE GRSS Image Analysis and Data Fusion School [Technical Committees]

   Gemine Vivone, Dalton Lunga, Francescopaolo Sica, and 3 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2023

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5. IEEE GRSM

   Analysis-ready data and FAIR-AI—Standardization of research collaboration and transparency across Earth-observation communities [technical committees]

   Dalton Lunga, Silvia Ullo, Ujjwal Verma, and 3 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2023

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6. IEEE GRSM

   There are no data like more data: Datasets for deep learning in earth observation

   Michael Schmitt, Seyed Ali Ahmadi, Yonghao Xu, and 4 more authors

   IEEE Geoscience and Remote Sensing Magazine, 2023

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7. Ecological Informatics

   Pattern detection and prediction using deep learning for intelligent decision support to identify fish behaviour in aquaculture

   S Shreesha, Manohara MM Pai, Radhika M Pai, and 1 more author

   Ecological Informatics, 2023

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8. IEEE IGARSS

   Improved semantic segmentation for identification of flooded regions in UAV aerial images: a transformer-based approach

   Ujjwal Verma and Gokul Puthumanaillam

   In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, 2023

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9. IEEE IGARSS

   ARD, FAIR Earth Observation Principles, Data Fusion: Where are we and where do we need to go?

   Dalton Lunga, Ronny Hänsch, Ujjwal Verma, and 3 more authors

   In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, 2023

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2022

1. IEEE CONECCT

   MartiNet: An Efficient Approach For River Segmentation In SAR Images

   Tejas Ravishankar, Trisha Celine Anil, Ujjwal Verma, and 2 more authors

   In 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2022

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2. IEEE IGARSS

   Recent trends and challenges in analysis of uav aerial images for post-disaster scene understanding

   Ujjwal Verma

   In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 2022

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3. IEEE IGARSS

   Comparison of Texture Classifiers with Deep Learning Methods for Flooded Region Identification in UAV Aerial Images

   Ujjwal Verma and Arsh Tangri

   In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 2022

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4. IEEE IGARSS

   Texture aware unsupervised segmentation for assessment of flood severity in uav aerial images

   Sushant Lenka, Bhavam Vidyarthi, Neil Sequeira, and 1 more author

   In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium, 2022

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5. CONECCT 2022

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

   Ashutosh Holla B, Manohara Pai M. M, Ujjwal Verma, and 1 more author

   In 2022 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), 2022

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2021

1. BDL-NeurIPS 2021

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

   Kumud Lakara, Akshat Bhandari, Pratinav Seth, and 1 more author

   2021

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   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 does not generally hold true in a natural setting. Usually, the deployment data is subject to various types of distributional shifts. The magnitude of a model’s performance is proportional to this shift in the distribution of the dataset. Thus it becomes necessary to evaluate a model’s uncertainty and robustness to distributional shifts to get a realistic estimate of its expected performance on real-world data. Present methods to evaluate uncertainty and model’s robustness are lacking and often fail to paint the full picture. Moreover, most analysis so far has primarily focused on classification tasks. In this paper, we propose more insightful metrics for general regression tasks using the Shifts Weather Prediction Dataset. We also present an evaluation of the baseline methods using these metrics

2. NAACL - SMM4H

   BERT based Transformers lead the way in Extraction of Health Information from Social Media

   Sidharth Ramesh, Abhiraj Tiwari, Parthivi Choubey, and 5 more authors

   In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, Jun 2021

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3. IEEE TENCON 2021

   Enhancement of PSNR based Anomaly Detection in Surveillance Videos using Penalty Modules

   Bhavam Vidyarthi, Neil Sequeira, Sushant Lenka, and 1 more author

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Jun 2021

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4. IEEE TENCON 2021

   Vehicle Re-identification in Smart City Transportation using Hybrid Surveillance Systems

   B Ashutosh Holla, M.M. Manohara Pai, Ujjwal Verma, and 1 more author

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Jun 2021

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5. IEEE TENCON 2021

   Anomaly Detection Using Classification CNN Models: A Video Analytic Approach

   Girisha S, Manohara Pai, Ujjwal Verma, and 2 more authors

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Jun 2021

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6. IEEE TENCON 2021

   Flood Magnitude Assessment from UAV Aerial Videos Based on Image Segmentation and Similarity

   Ananya Sharma and Ujjwal Verma

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Jun 2021

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7. IEEE TENCON 2021

   Behavioural Pattern Analysis of Fishes for Smart Aquaculture: An Object Centric Approach

   Shreesha S, Manohara Pai M. M, Ujjwal Verma, and 2 more authors

   In TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON), Jun 2021

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8. IEEE MysuruCon 2021

   Exploring Techniques for Photo-realistic Image Generation from 3D Models - A Deep Learning Approach

   Pranoy Ghosh, Krithika M Pai, Manohara Pai M M, and 4 more authors

   In 2021 IEEE Mysore Sub Section International Conference (MysuruCon), Jun 2021

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9. IEEE CONNECT 2021

   LSTM-Based Prediction of Water Quality Parameters System in Backwaters

   Shreesha S, Manohara Pai M M, Radhika M Pai, and 1 more author

   In 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT), Jun 2021

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10. IEEE MASCON 2021

    Semantic Segmentation with Enhanced Temporal Smoothness Using CRF in Aerial Videos

    S Girisha, Manohara M.M Pai, Ujjwal Verma, and 1 more author

    In 2021 IEEE Madras Section Conference (MASCON), Jun 2021

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11. CAGEO

    DeepRivWidth : Deep learning based semantic segmentation approach for river identification and width measurement in SAR images of Coastal Karnataka

    Ujjwal Verma, Arjun Chauhan, Manohara Pai M.M., and 1 more author

    Computers & Geosciences, Jun 2021

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12. IEEE JSTARS

    UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information

    S Girisha^*, Ujjwal Verma^*, Manohara Pai Pai, and 1 more author

    IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Jun 2021

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    Semantic segmentation of aerial videos has been extensively used for decision making in monitoring environmental changes, urban planning, and disaster management. The reliability of these decision support systems is dependent on the accuracy of the video semantic segmentation algorithms. The existing CNN based video semantic segmentation methods have enhanced the image semantic segmentation methods by incorporating an additional module such as LSTM or optical flow for computing temporal dynamics of the video which is a computational overhead. The proposed research work modifies the CNN architecture by incorporating temporal information to improve the efficiency of video semantic segmentation. In this work, an enhanced encoder-decoder based CNN architecture (UVid-Net) is proposed for UAV video semantic segmentation. The encoder of the proposed architecture embeds temporal information for temporally consistent labelling. The decoder is enhanced by introducing the feature-refiner module, which aids in accurate localization of the class labels. The proposed UVid-Net architecture for UAV video semantic segmentation is quantitatively evaluated on extended ManipalUAVid dataset. The performance metric mIoU of 0.79 has been observed which is significantly greater than the other state-of-the-art algorithms. Further, the proposed work produced promising results even for the pre-trained model of UVid-Net on urban street scene with fine tuning the final layer on UAV aerial videos.

13. IEEE Access

    Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

    Shantam Shorewala, Armaan Ashfaque ^*, R Sidharth ^*, and 1 more author

    IEEE Access, Jun 2021

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2020

1. RSC Adv.

   Thread integrated smart-phone imaging facilitates early turning point colorimetric assay for microbes

   Anusha Prabhu, Giri Nandagopal, Prakash Peralam Yegneswaran, and 3 more authors

   RSC Advances, Jun 2020

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2. Improved Semantic Segmentation of Water Bodies and Land in SAR Images Using Generative Adversarial Networks

   Manohara Pai, Vaibhav Mehrotra, Ujjwal Verma, and 1 more author

   International Journal of Semantic Computing, Jun 2020

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   The availability of computationally efficient and powerful Deep Learning frameworks and high-resolution satellite imagery has created new approach for developing complex applications in the field of remote sensing. The easy access to abundant image data repository made available by different satellites of space agencies such as Copernicus, Landsat, etc. has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. The challenging research problem in this direction is the accurate identification and subsequent segmentation of surface water in images in the microwave spectrum. In the recent years, deep learning methods for semantic segmentation are the preferred choice given its high accuracy and ease of use. One major bottleneck in semantic segmentation pipelines is the manual annotation of data. This paper proposes Generative Adversarial Networks (GANs) on the training data (images and their corresponding labels) to create an enhanced dataset on which the networks can be trained, therefore, reducing human effort of manual labeling. Further, the research also proposes the use of deep-learning approaches such as U-Net and FCN-8 to perform an efficient segmentation of auto annotated, enhanced data of water body and land. The experimental results show that the U-Net model without GAN achieves superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.9923. However, when augmented with GANs, the results saw a rise in these metrics with PA of 0.99 and F1 score of 0.9954.

3. Computer Vision Based Fish Tracking And Behaviour Detection System

   S Shreesha, Manohara Pai, Ujjwal Verma, and 1 more author

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Jun 2020
4. Efficient Vehicle Counting by Eliminating Identical Vehicles in UAV aerial videos

   Ashutosh Holla, Manohara Pai, Ujjwal Verma, and 1 more author

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Jun 2020
5. Semantic Segmentation of UAV Videos based on Temporal Smoothness in Conditional Random Fields (BEST PAPER AWARD)

   S. Girisha, Manohara Pai, Ujjwal Verma, and 1 more author

   In 2020 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER), Jun 2020

2019

1. Automatic segmentation of river and land in sar images: A deep learning approach

   Manohara Pai, Vaibhav Mehrotra, Shreyas Aiyar, and 2 more authors

   In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Jun 2019

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   The ubiquitousness of satellite imagery and powerful, computationally efficient Deep Learning frameworks have found profound use in the field of remote sensing. Augmented with easy access to abundant image data made available by different satellites such as LANDSAT and European Space Agency’s Copernicus missions, deep learning has opened various avenues of research in monitoring the world’s oceans, land, rivers, etc. One significant problem in this direction is the accurate identification and subsequent segmentation of surface-water in images in the microwave spectrum. Typically, standard image processing tools are used to segment the images which are time-inefficient. However, in recent years, deep learning methods for semantic segmentation is the preferred choice given its high accuracy and ease of use. This paper proposes the use of deep-learning approaches such as U-Net to perform an efficient segmentation of river and land. Experimental results show that our approach achieves vastly superior performance on SAR images with pixel accuracy of 0.98 and F1 score of 0.99.

2. Semantic segmentation of UAV aerial videos using convolutional neural networks

   S. Girisha, Manohara Pai, Ujjwal Verma, and 1 more author

   In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Jun 2019

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   Semantic segmentation of complex aerial videos enables a better understanding of scene and context. This enhances the performance of automated video processing techniques like anomaly detection, object detection, event detection and other applications. But, there is a limited study of semantic segmentation in aerial videos due to non-availability of the relevant dataset. To address this, an aerial video dataset is captured using DJI Phantom 3 professional drone and is annotated manually. In addition, the proposed research work investigates the performance of semantic segmentation algorithms for aerial videos implemented using Fully Convolution Networks (FCN) and U-net architectures. In this study, two classes (greenery, road) are considered for semantic segmentation. It is observed that both architectures perform competitively on the aerial videos of Unmanned Aerial Vehicle (UAV) with a pixel accuracy of 89.7% and 87.31% respectively.

3. IEEE Access

   Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)

   S Girisha, Manohara Pai, Ujjwal Verma, and 1 more author

   IEEE Access, Jun 2019

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   Semantic segmentation of videos helps in scene understanding, thereby assisting in other automated video processing techniques like anomaly detection, object detection, event detection, etc. However, there has been limited study on semantic segmentation of videos acquired using Unmanned Aerial Vehicles (UAV), primarily due to the absence of standard dataset. In this paper, a new UAV aerial video dataset (ManipalUAVid) for semantic segmentation is presented. The videos have been acquired in a closed university campus, and fine annotation is provided for four background classes viz. constructions, greeneries, roads, and waterbodies. Also, the performance of four semantic segmentation approaches: Conditional Random Field (CRF), U-Net, Fully Convolutional Network (FCN) and DeepLabV3+ are analysed on ManipalUAVid dataset. It is seen that these algorithms perform competitively on UAV aerial video dataset and achieves an mIoU of 0.86, 0.86, 0.86 and 0.83 respectively

2015

1. EURASIP JIVP

   Segmentation and size estimation of tomatoes from sequences of paired images

   Ujjwal Verma, Florence Rossant, and Isabelle Bloch

   EURASIP Journal on Image and Video Processing, Jun 2015

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2. Segmentation of tomatoes in open field images with shape and temporal constraints

   Ujjwal Verma, Florence Rossant, Isabelle Bloch, and 2 more authors

   In Pattern Recognition Applications and Methods, Jun 2015

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2014

1. ICPRAM

   Shape-based segmentation of tomatoes for agriculture monitoring

   Ujjwal Verma, Florence Rossant, Isabelle Bloch, and 2 more authors

   In International Conference on Pattern Recognition Applications and Methods (ICPRAM), Jun 2014

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2. Tomato development monitoring in an open field, using a two-camera acquisition system

   Ujjwal Verma, Florence Rossant, Isabelle Bloch, and 3 more authors

   In 12th International Conference on Precision Agriculture, Jun 2014

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2008

1. T-Law: A new suggestion for signal companding

   Sunil Bhooshan, Vinay Kumar, Ujjwal Verma, and 2 more authors

   In 2008 Congress on Image and Signal Processing, Jun 2008