Presenter: Prof. Dr. Faisal Shafait, Associate Dean, Department of Computing, SEECS, National University of Sciences and Technology – Pakistan.
Datum: 24 november klockan 14:00-15:00.
Abstract: Forest change detection is crucial for sustainable forest management. The changes in the forest area due to deforestation (such as wildfires or logging due to development activities) or afforestation alter the total forest area. Additionally, it impacts the available stock for commercial purposes, climate change due to carbon emissions, and biodiversity of the forest habitat estimations, which are essential for disaster management and policy making. In recent years, foresters have relied on hand crafted features or bi-temporal change detection methods to detect change in the remote sensing imagery to estimate the forest area. Due to manual processing steps, these methods are fragile and prone to errors and can generate inaccurate (i.e., under or over) segmentation results. In the talk, I will present AI-ForestWatch, an open source framework for forest estimation and change analysis utilizing deep learning on multi-spectral space-borne images.
Short bio: Prof. Dr. Faisal Shafait has 15+ years of research and teaching experience in Artificial Intelligence and Machine Learning with the primary focus on computer vision and document image analysis. He completed his PhD in Computer Engineering from Technical University of Kaiserslautern (TUKL), Germany in 2008 with highest distinction. Currently, he is working as a Professor and is heading the Department of Computing at School of Electrical Engineering and Computer Science (SEECS), NUST, Pakistan as well as the Deep Learning Lab (DLL) under National Center of Artificial Intelligence (NCAI), NUST, Pakistan. Dr. Faisal has made a significant international reputation in the field of Artificial Intelligence by earning 10,900+ citations with i10 and h indices of 152 and 52 (Google Scholar) respectively. He received the prestigious IAPR/ICDAR Young Investigator Award by the International Association of Pattern Recognition (IAPR) in 2019 and has recently been included in the list of the World’s Top 2% Scientists compiled by Stanford University.