An integrated object-based-deep learning approach applied for mapping armed conflict impacts and land scars

The increasing number of wars and armed conflicts worldwide demands the development of an efficient, cost-effective, and transferable methodological framework for mapping environmental impacts and land scars. This state-of-the-art research develops a novel and transferable data-driven approach for mapping war and armed conflict scars and impacts. A variety of war land scars were determined for the Iran-Iraq war using integrated object-based image analysis (OBIA) and deep learning convolutional neural networks (DL-CNNs). Then, the efficiency and transferability of the approach are examined for the Syrian civil war and the Karabakh (Azerbaijan-Armenia) conflict. To validate the results and examine the transferability of the approach and its efficiency, an integrated fuzzy synthetic evaluation (FSE) and quantity and allocation disagreement index (QADI) were applied. The overall result of an accuracy assessment using FSE-QADI yielded an accuracy of 0.96 for the obtained land scars in the case of the Iran-Iraq war, and the transferability of the developed OBIA-DL-CNNs was demonstrated with overall accuracies of 0.93 and 0.94 for the Syrian and Karabakh land scars, respectively. Recent progress in earth observation technology and the availability of a variety of products demand the development of efficient and transferable data-driven approaches. Thus, as a state of the art, the current study makes a significant contribution to the domain of remote sensing by developing an integrated transferable approach of OBIA-DL and FSE-QADI. The proposed approach will support authorities and stakeholders in detecting and mapping the impacts of armed conflicts in battle areas to enhance the environmental aspect of armed conflict operations, which may be helpful in preventing future conflicts. • A novel, efficient and transferable approach for war impacts and land scars mapping is proposed. • An integrated object-based image analysis and deep learning approach is applied. • A robust methodology for armed conflict impact assessment is developed.