Spatial Analytics & machine Intelligence

Spatial Analytics and Machine Intelligence Lab

Head: Dr. Arun PV

Email: arun.pv@iiits.in

Objective

The goal of the Spatial Analytics and Machine Intelligence Lab is to advance the state-of-the-art in machine intelligence and spatial data analysis. Our goal is to create novel techniques and algorithms that make use of spectral and spatial data for a range of applications, such as image processing, geographic information systems (GIS), and remote sensing, data warehousing, knowledge
representation, etc. With the use of cutting-edge research in deep learning, earth and lunar surface observation, representation of knowledge, and sophisticated computational approaches, we want to address important spatial analytics concerns like super- resolution, data fusion, and information extraction from satellite images. By developing solutions that enhance the precision, effectiveness, and practicality of spatial data analysis, we hope to benefit the larger scientific community.

Faculty

Sponsored Projects

Publications

• Soorya. S., P.V. Arun., A. Porwal, Guneshwar. T., 2023. Spectral unmixing of hyperspectral images: Implications for
  estimating mineral abundances from Hyperspectral data. Indian Planetary Science Conference (IPSC-2023),Indian
  Planetary Science Association(IPSA).
• Soorya. S., P.V. Arun., 2023. Spectral Unmixing In Generative Space: 3D-GAN Based Approach. 13th Workshop on
  Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS).
• Soorya. S., P.V. Arun., 2023. Unmixing In Latent Space: A Novel Unsupervised Approach For Geological Mapping Of
  Lunar Surface. IEEE India Geoscience and Remote Sensing Symposium (InGARSS).
• Soorya. S., P.V. Arun., 2023. Interpretable Spectral Unmixing Approaches for IIRS Data Onboard Chandrayaan -2
  Mission. American Geophysical Union (AGU23).
• S. B and A. PV, “Decoding the Moon's Surface: A Graph Neural Network Based Analysis of Chandrayaan-2 Lunar Data
  Classification,” IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA,
  USA, 2023, pp. 4210-4213, doi: 10.1109/IGARSS52108.2023.10282465.
• Samrat B, P.V Arun, A. Porwal, “Advancing Hyperspectral Image Quality: Deep Learning-Driven Denoising with Sure
  Loss Function for Chandrayaan-2 IIRS Dataset”, IEEE Workshop on Hyperspectral Image and Signal Processing:
  Evolution in Remote Sensing 2023.
• Samrat B, P.V Arun, “Latent Space Graph Convolutional Networks for Accurate Classification of Chandrayaan-2 Lunar
  Hyperspectral Images”, NASA Exploration Science Forum 2023.
• P.V. Arun, Suresh, S., Sahoo, M. M., and A. Porwal, “Interpretability and Explainability of Hyperspectral Image
  Classification Techniques for Lunar Surface Mapping”, NASA Exploration Science Forum 2023.
• G. Akhil, P.V. Arun, A. Porwal, “Efficient Graph Formulation and Latent Space Integration for Lunar Hyperspectral
  Image Classification.”, IEEE Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing
  2023.
• G. Akhil, P.V. Arun, “Enhancing Hyperspectral Classification through Graph Convolutional Networks with Adaptive
  Graph Construction”, NASA Exploration Science Forum 2023.
• K. Sarat, P.V. Arun, “Spatial-Spectral Attention for Geological Mapping of Hyperspectral Sensor on Board Chandrayaan-2 Mission”, International Geoscience and Remote Sensing Symposium (IEEE IGARSS 2023).
• Adarsh N L, P.V Arun, A. Porwal, Dr. Malcolm Aranha, “A Journey Within: Unraveling Model Internal Representations
  for Magnetic Image Classification”, American Geophysical Union (AGU23).
• P. V. Arun, Maitreya Mohan Sahoo, Alok Porwal, “Graph Neural Network Based Interpretable Spectral Unmixing for
  Hyperspectral Unmixing Hyperspectral IIRS Data Onboard Chandrayaan-2 Mission”, IGARSS 2023 - 2023 IEEE
  International Geoscience and Remote Sensing Symposium
• Palle Pranay Reddy, Ram Gopal, MS Dheeraj, Soorya Suresh, Arun PV,” Accelerating HybridSN With Dynamic Step Quantification For HSI Classification”, 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
• Palle Pranay Reddy, Ram Gopal, MS Dheeraj, Arun PV, “Morphological CNN Combined With Noise Inclined Module And Denoising Framework”, 2024 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).
• P V Arun, A Karnieli, “Reinforced deep learning approach for analyzing spaceborne-derived crop phenology” 2024 International Journal of Applied Earth Observation and Geoinformation
• Xuguang Zhu, Haorui Zhang, Bin Hu, Kunpeng Huang, Pattathal V Arun, Xiuping Jia, Dong Zhao, Qing Wang, Huixin Zhou, Shuowen Yang, “DSP Net: A dynamic spectral-spatial joint perception network for hyperspectral target tracking” 2023, IEEE Geoscience and Remote Sensing Letters.
• Arun PV, Alok Porwal, Malcolm Aranha, “Leveraging Internal Representations of Model for Magnetic Image
  Classification” 2024 arXiv preprint arXiv:2403.0679
• Sahoo, M.M.; Kalimuthu, R.; PV, A.; Porwal, A.; Mathew, S.K. Modeling Spectral Unmixing of Geological Mixtures: An Experimental Study Using Rock Samples. Remote Sens. 2023, 15, 3300.
• Amal S Namboodiri, Rakesh Kumar Sanodiya, PV Arun, “Remote Sensing Cloud Removal using a Combination of Spatial Attention and Edge Detection” 2023 11th International Symposium on Electronic Systems Devices and Computing (ESDC).
• Arun Pattathal V., and Arnon Karnieli. "Deep feature learning and latent space encoding for crop phenology analysis."
  Expert Systems with Applications 187 (2022): 115929. ISSN 0957-4174.

Other Prominent Details

Workshops:

• 1. SERB sponsored Workshop on Spatial Data Analytics
• 2. SERB sponsored Karyashala on Geospatial Intelligence
• 3. ATAL sponsored Faculty Development Programme on Spatial Analytics & Block Chain
• 4. SERB Sponsored Workshop on Advanced Deep learning and Applications (WADLA 2.0)
• 5. SERB Sponsored Workshop on Advanced Deep Learning and Applications (WADLA 3.0)