{"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/photi-lakeice-dataset","title":"Photi-LakeIce Dataset","arxiv_id":null,"date":"2020-02-18","proceeding":"ISPRS Congress 2020 2","authors":["Rajanie Prabha","Manu Tom","Mathias Rothermel","Emmanuel Baltsavias","Laura Leal-Taixe","Konrad Schindler"],"abstract":"Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatio-temporal information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and different lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves intersection-over-union (IoU) values of ~71% across different cameras and ~69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing web sites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations.","url_abs":"https://arxiv.org/abs/2002.07875v1","url_pdf":"https://arxiv.org/pdf/2002.07875.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":"photi-lakeice-dataset","repo_url":"https://github.com/czarmanu/photi-lakeice-dataset","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":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lake-detection","task_name":"Lake Detection"},{"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":[{"method_slug":"crf","method_name":"CRF"},{"method_slug":"deeplab","method_name":"DeepLab"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"feedforward-network","method_name":"Feedforward Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}