{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lake-ice-monitoring-with-webcams","title":"LAKE ICE MONITORING WITH WEBCAMS","arxiv_id":null,"date":"2018-05-28","proceeding":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences 2018 5","authors":["M. Xiao","M. Rothermel","M. Tom","S. Galliani","E. Baltsavias","and K. Schindler"],"abstract":"Continuous monitoring of climate indicators is important for understanding the dynamics and trends of the climate system. Lake ice has been identified as one such indicator, and has been included in the list of Essential Climate Variables (ECVs). Currently there are two main ways to survey lake ice cover and its change over time, in-situ measurements and satellite remote sensing. The challenge with both of them is to ensure sufficient spatial and temporal resolution. Here, we investigate the possibility to monitor lake ice with video streams acquired by publicly available webcams. Main advantages of webcams are their high temporal frequency and dense spatial sampling. By contrast, they have low spectral resolution and limited image quality. Moreover, the uncontrolled radiometry and low, oblique viewpoints result in heavily varying appearance of water, ice and snow. We present a workflow for pixel-wise semantic segmentation of images into these classes, based on state-of-the-art encoder-decoder Convolutional Neural Networks (CNNs). The proposed segmentation pipeline is evaluated on two sequences featuring different ground sampling distances. The experiment suggests that (networks of) webcams have great potential for lake ice monitoring. The overall per-pixel accuracies for both tested data sets exceed 95 %. Furthermore, per-image discrimination between ice-on and ice-off conditions, derived by accumulating per-pixel results, is 100 % correct for our test data, making it possible to precisely recover freezing and thawing dates.","url_abs":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2/311/2018/","url_pdf":"https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-2/311/2018/isprs-annals-IV-2-311-2018.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"lake-ice-monitoring-with-webcams","repo_url":"https://github.com/czarmanu/tiramisu_keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"change-detection-for-remote-sensing-images","task_name":"Change detection for remote sensing images"},{"task_slug":"lake-ice-detection","task_name":"Lake Ice Monitoring"},{"task_slug":"remote-sensing-image-classification","task_name":"Remote Sensing Image Classification"},{"task_slug":"segmentation-of-remote-sensing-imagery","task_name":"Segmentation Of Remote Sensing Imagery"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"the-semantic-segmentation-of-remote-sensing","task_name":"The Semantic Segmentation Of Remote Sensing Imagery"},{"task_slug":"webcam-rgb-image-classification","task_name":"Webcam (RGB) image classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}