This is multisource remote sensing data consist of 10 bands (Sentinel-2 optical bands (Blue, Green, Red, NIR, NDWI), Sentinel-1 derived SAR Coherence, Landsat8 Thermal band, Slope and Elevation) and corresponding labels (lake boundaries) collected in Himalayas. Please cite our article Saurabh Kaushik, Tejpal Singh, P.K. Joshi, Andreas J. Dietz,Automated mapping of glacial lakes using multisource remote sensing data and deep convolutional neural network,International Journal of Applied Earth Observation and Geoinformation,Volume 115,2022,103085,ISSN 1569-8432,https://doi.org/10.1016/j.jag.2022.103085.(https://www.sciencedirect.com/science/article/pii/S1569843222002734)Abstract: The characteristics of glacial lakes are a precursor to glacier retreat, ice mass loss, velocity, and potential risk of Glacial Lake Outburst Floods (GLOF). The current state of the art for glacial lake mapping, especially in a high mountainous region, is limited to manual or semi-automated threshold-based methods. Here, we propose a fully automated novel approach for glacial lake mapping using a Deep Convolutional Neural Network (DCNN) and remote sensing data originating from various sources. A combination of these multisource remote sensing data (i.e., multispectral, thermal, microwave, and a Digital Elevation Model) is fed to the fully connected DCNN. The DCNN architecture, namely GLNet, is designed by choosing an optimum number and size of convolutional layers, filters, and other hyperparameters. Our proposed GLNet is trained on 660 images covering twelve sites spread across diverse climatic and topographic regions of the Himalaya. The robustness of the model is tested over three sites in the Eastern Himalaya and one site in the Western Himalaya. The classification results outperform the existing state-of-the-art datasets by achieving 0.98 accuracy, 0.95 precision, 0.95 recall, and 0.95 F- score over the test data. The results over test sites (F-score test site1: 0.91, test site 2: 0.80, test site3: 0.97, and test site4: 0.70) showed promising results and spatiotemporal transferability of the proposed method. The coefficient of determination (R2) between GLNet predicted lake boundaries and reference lake boundaries exhibits excellent results (0.90). The study provides proof of concept for automated glacial mapping for large geographical regions via integrated capabilities of deep convolutional neural networks and multisource remote sensing data.Keywords: Convolutional Neural Network; Glacial lakes; Remote sensing; Himalaya