Background: The last few decades have witnessed a massive increase in Big Geodata from space-borne and airborne sensors. The goal is to provide large-scale, homogeneous information processing from these ever increasing multi-modal data sources that require automated and intelligent frameworks for storage and analysis. The resurgence of machine learning and deep neural networks have revolutionised computational methods and have found wide applications in the land cover and land use classification, content-based information retrieval, region of interest detection and temporal analysis of continuously evolving remote sensing data. Due to the availability of data from many satellite missions in public domain, certain multi-modal approaches such as multi-sensor data fusion or pan-sharpening and cross-modal information retrieval are of extreme importance. Earth observation tasks also entail the necessity for various location-based services, online mapping services, surveillance, crop-monitoring, soil-moisture tracking, change detection applications, etc. Our specific topics of research interests in Computational Intelligence in Remote Sensing Data/Image Analysis include:

• • • Machine learning and Deep learning in Geospatial Science

    • Image understanding and synthesis

    • Image classification using Supervised and Unsupervised learning/Clustering

    • Time-series forecasting models
      

    • Spatio-temporal models
      

    • Urban growth modelling

    • Urban climate
      

    • Digital agriculture

    • SAR data analysis
      

    • UAV data analysis

    • Multi-modal Data Fusion

    • Target detection and Segmentation

    • Spectral and Spatial methods

    • Spectral unmixing

    • Noise recognition and filtering

    • Change detection