Proceedings of the 18th International Symposium on Spatial and Temporal Data最新文献_第3页

Harmonization-guided deep residual network for imputing under clouds with multi-sensor satellite imagery 基于协调制导的多传感器卫星云图深度残差网络

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609967

Xian Yang, Yifan Zhao, Ranga Raju Vatsavai

{"title":"Harmonization-guided deep residual network for imputing under clouds with multi-sensor satellite imagery","authors":"Xian Yang, Yifan Zhao, Ranga Raju Vatsavai","doi":"10.1145/3609956.3609967","DOIUrl":"https://doi.org/10.1145/3609956.3609967","url":null,"abstract":"Multi-sensor spatiotemporal satellite images have become crucial for monitoring the geophysical characteristics of the Earth’s environment. However, clouds often obstruct the view from the optical sensors mounted on satellites and therefore degrade the quality of spectral, spatial, and temporal information. Though cloud imputation with the rise of deep learning research has provided novel ways to reconstruct the cloud-contaminated regions, many learning-based methods still lack the capability of harmonizing the differences between similar spectral bands across multiple sensors. To cope with the inter-sensor inconsistency of overlapping bands in different optical sensors, we propose a novel harmonization-guided residual network to impute the areas under clouds. We present a knowledge-guided harmonization model that maps the reflectance response from one satellite collection to another based on the spectral distribution of the cloud-free pixels. The harmonized cloud-free image is subsequently exploited in the intermediate layers as an additional input, paired with a custom loss function that considers image reconstruction quality and inter-sensor consistency jointly during training. To demonstrate the performance of our model, we conducted extensive experiments on a multi-sensor remote sensing imagery benchmark dataset consisting of widely used Landsat-8 and Sentinel-2 images. Compared to the state-of-the-art methods, results show at least a 22.35% improvement in MSE.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121838390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DEAR: Dynamic Electric Ambulance Redeployment 亲爱的:动态电动救护车重新部署

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609959

Lukas Rottkamp, Niklas Strauß, M. Schubert

{"title":"DEAR: Dynamic Electric Ambulance Redeployment","authors":"Lukas Rottkamp, Niklas Strauß, M. Schubert","doi":"10.1145/3609956.3609959","DOIUrl":"https://doi.org/10.1145/3609956.3609959","url":null,"abstract":"Dynamic Ambulance Redeployment (DAR) is the task of dynamically assigning ambulances after incidents to base stations to minimize future response times. Though DAR has attracted considerable attention from the research community, existing solutions do not consider using electric ambulances despite the global shift towards electric mobility. In this paper, we are the first to examine the impact of electric ambulances and their required downtime for recharging to DAR and demonstrate that using policies for conventional vehicles can lead to a significant increase in either the number of required ambulances or in the response time to emergencies. Therefore, we propose a new redeployment policy that considers the remaining energy levels, the recharging stations’ locations, and the required recharging time. Our new method is based on minimizing energy deficits (MED) and can provide well-performing redeployment decisions in the novel Dynamic Electric Ambulance Redeployment problem (DEAR). We evaluate MED on a simulation using real-world emergency data from the city of San Francisco and show that MED can provide the required service level without additional ambulances in most cases. For DEAR, MED outperforms various established state-of-the-art solutions for conventional DAR and straightforward solutions to this setting.","PeriodicalId":274777,"journal":{"name":"Proceedings of the 18th International Symposium on Spatial and Temporal Data","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114674934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Viper: Interactive Exploration of Large Satellite Data✱✱ Viper:大型卫星数据的交互探索

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 2023-08-23 DOI: 10.1145/3609956.3609966

Zhuocheng Shang, A. Eldawy

引用次数: 0

Proceedings of the 18th International Symposium on Spatial and Temporal Data 第十八届时空数据国际研讨会论文集

Proceedings of the 18th International Symposium on Spatial and Temporal Data Pub Date : 1900-01-01 DOI: 10.1145/3609956

引用次数: 0