AI for Remote Sensing


Dr. Dodi Sudiana

A crucial role is played by Artificial Intelligence (AI) in the field of remote sensing, where it sees a revolution in the analysis and interpretation of vast amounts of geospatial data. The utilization of AI techniques, such as machine learning and deep learning, allows for the automated and efficient extraction of valuable information from remote sensing imagery. The ability to identify and classify objects, detect changes in the landscape, and predict environmental trends is significantly enhanced by these technologies. Large datasets can be processed quickly, thereby improving the accuracy and speed of image analysis for applications ranging from agriculture and forestry monitoring to disaster response and urban planning. The power of automation is harnessed through the use of AI for remote sensing, enabling the derivation of meaningful insights from Earth observation data and contributing to more informed decision-making and sustainable resource management. 

Representative of outcomes


A Hybrid Convolutional Neural Network and Random Forest for Burned Area Identification with Optical and Synthetic Aperture Radar (SAR) Data
Author: Sudiana D.; Lestari A.I.; Riyanto I.; Rizkinia M.; Arief R.; Prabuwono A.S.; Sri Sumantyo J.T.


Published in: Remote Sensing (Vol. 15(3))
Date of Publication: 2023
DOI: 10.3390/rs15030728

A hyperspectral anomaly detection algorithm based on morphological profile and attribute filter with band selection and automatic determination of maximum area
Author: Andika F.; Rizkinia M.; Okuda M.


Published in: Remote Sensing (Vol. 12(20))
Date of Publication: 2020
DOI: 10.3390/rs12203387