Research Interests

Keywords: Deep Learning, Computer Vision, Satellite Imagery, Remote Sensing, Weather Forecast

My research pursuits converge at the nexus of advanced technology and environmental science, with a dedicated focus on innovating precise forecasting solutions. I utilize advanced technology to comprehend environmental changes, contributing to a sustainable future. Furthermore, I am enthusiastic about formulating methodologies that leverage deep learning and computer vision techniques to address the impacts of climate change. Some specific project areas and applications include:

  • Development of adaptive and non adaptive land cover monitoring by analyzing remotely sensed images
  • Quantifying Climate Change Impacts through Innovative Change Detection Approaches
  • Enhancing Climate Resilience: Innovative Approaches to Risk Estimation in the Face of Climate Change

Research Experience

  • Postdoctoral Research Associate (1st August 2023 - ongoing)

    Computing and Data Sciences, Boston University, Massachusetts, USA

  • Postdoctoral Research Associate (8th June 2022 - 7th June 2023)

    CYENS Centre of Excellence (SuPerWorld MRG), Nicosia, Cyprus

  • Assistant Project Engineer (3th April 2014 - 4th July 2015)

    Indian Institute of Technology Guwahati, Assam, India

  • Research Intern (15th December 2020 - 31th May 2022)

    CYENS Centre of Excellence (SuPerWorld MRG), Nicosia, Cyprus

Selected Projects

Adaptive Land Cover Classification

Deep learning based adaptive land cover monitoring by analyzing remotely sensed images

Land Use Land Cover Change Detection

Land Use Change Detection Using Deep Siamese Neural Networks and Weakly Supervised Learning

H2OforAll

Horizon Europe project H2OforAll “Innovative Integrated Tools and Technologies to Protect and Treat Drinking Water from Disinfection Byproducts (DBPs)” (total budget of €3,452,700.50, Partners organizations 17)funded by the European Union (project lead on be half of CYENS)

Detection of Area Estimation using satellite images

Detect the area occupied by swimming pools/buildings inside the geographical area under study based on their canopy from high spatial resolution satellite imagery, using advanced, state-of-the-art proprietary AI models.

Rainfall Forecasting (ongoing)

To predicting the amount, timing, and distribution of rainfall in a specific area. It utilizes observations from weather stations, satellites, and radar, along with, remote sensing, climate data, and increasingly, machine learning.

Flood Risk Estimation (ongoing)

The assessment of the likelihood and potential impact of flooding in an area, incorporating factors like topography, weather, land use, and infrastructure.

Publications

Journals

  • Kalita, I., Singh, G. P., & Roy, M. (2023). Crop classification using aerial images by analyzing an ensemble of DCNNs under multi-filter & multi-scale framework. Multimedia Tools and Applications, 82(12), 18409-18433.
  • Kalita, I., & Roy, M. (2022). Class-Wise Subspace Alignment-Based Unsupervised Adaptive Land Cover Classification in Scene-Level Using Deep Siamese Network. IEEE Transactions on Neural Networks and Learning Systems.
  • Kalita, I., Kumar, R. N. S., & Roy, M. (2021). Deep learning-based cross-sensor domain adaptation under active learning for land cover classification. IEEE Geoscience and Remote Sensing Letters, 19, 1-5.
  • Kalita, I., & Roy, M. (2020). Deep neural network-based heterogeneous domain adaptation using ensemble decision making in land cover classification. IEEE Transactions on Artificial Intelligence, 1(2), 167-180.

Conferences

  • Jamil, A., Padubidri, C., Karatsiolis, S., Kalita, I., Guley, A., & Kamilaris, A. GAEA-A Country-Scale Geospatial Environmental Modelling Tool: Towards a Digital Twin for Real Estate.
  • Kalita, I., Mugganawar, N., & Roy, M. (2022, July). Unsupervised cross-sensor domain adaptation using adversarial network for land cover classification. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5724-5727). IEEE.
  • Kalita, I., & Roy, M. (2022, July). Inception time DCNN for land cover classification by analyzing multi-temporal remotely sensed images. In IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium (pp. 5736-5739). IEEE.
  • Kalita, I., & Roy, M. (2022, July). Deep learning method for agriculture monitoring under adaptive environment using UAV-based aerial images. In 2022 IEEE Region 10 Symposium (TENSYMP) (pp. 1-6). IEEE.
  • Kalita, I., Karatsiolis, S., & Kamilaris, A. (2021). Land use change detection using deep siamese neural networks and weakly supervised learning. In Computer Analysis of Images and Patterns: 19th International Conference, CAIP 2021, Virtual Event, September 28–30, 2021, Proceedings, Part II 19 (pp. 24-35). Springer International Publishing.
  • Chakraborty, S., Kalita, I., & Roy, M. (2021). An Adversarial Learning Mechanism for Dealing with the Class-Imbalance Problem in Land-Cover Classification. In Hybrid Intelligent Systems: 19th International Conference on Hybrid Intelligent Systems (HIS 2019) held in Bhopal, India, December 10-12, 2019 19 (pp. 188-196). Springer International Publishing.
  • Kalita, I., Chakraborty, S., & Roy, M. (2019, December). Deep ensemble network for handling class-imbalance problem in land-cover classification. In 2019 International Conference on Information Technology (ICIT) (pp. 505-509). IEEE.
  • Chakraborty, S., Kalita, I., & Roy, M. (2019, July). Unsupervised domain adaptation in land-cover classification under neural approach using feature-level ensemble. In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (pp. 724-727). IEEE.

For all the publications please see my google scholar page.

Interested for colaboration? Please don't hesitate to email me at indrajit@bu.edu, providing details about your background and research interests.

Want to know more about my projects? Kindly email me at indrajit@bu.edu, using the title of the project or research paper as the subject.