气候变化领域集成服务门户(CCPortal): Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)

CCPortal

DOI	10.3390/land13010080
	Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)
	Orusa, Tommaso; Viani, Annalisa; Borgogno-Mondino, Enrico; Suziedelyte-Visockiene, Jurate
发表日期	2024
EISSN	2073-445X
起始页码	13
结束页码	1
卷号	13 期号:1
英文摘要	Nowadays, European program Copernicus' Sentinel missions have allowed the development of several application services. In this regard, to strengthen the use of free satellite data in ordinary administrative workflows, this work aims to evaluate the feasibility and prototypal development of a possible service called Sen4MUN for the distribution of contributions yearly allocated to local municipalities and scalable to all European regions. The analysis was focused on the Aosta Valley region, North West Italy. A comparison between the Ordinary Workflow (OW) and the suggested Sen4MUN approach was performed. OW is based on statistical survey and municipality declaration, while Sen4MUN is based on geospatial deep learning techniques on aerial imagery (to extract roads and buildings to get real estate units) and yearly Land Cover map components according to European EAGLE guidelines. Both methods are based on land cover components which represent the input on which the financial coefficients for assigning contributions are applied. In both approaches, buffers are applied onto urban class (LCb). This buffer was performed according to the EEA-ISPRA soil consumption guidelines to avoid underestimating some areas that are difficult to map. In the case of Sen4MUN, this is applied to overcome Sentinel sensor limits and spectral mixing issues, while in the case of OW, this is due to limits in the survey method itself. Finally, a validation was performed assuming as truth the approach defined by law as the standard, i.e., OW, although it has limitations. MAEs involving LCb, road lengths and real estate units demonstrate the effectiveness of Sen4MUN. The developed approach suggests a contribution system based on Geomatics and Remote sensing to the public administration.
英文关键词	Sen4MUN; geomatics for public administration; Sentinel-1 & Sentinel-2; AGEA orthophoto; ArcGIS Pro; land cover; money assignment to local entities; Europe; Italy; Alpine region
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Environmental Studies
WOS记录号	WOS:001153003100001
来源期刊	LAND
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/302287
作者单位	University of Turin
推荐引用方式 GB/T 7714	Orusa, Tommaso,Viani, Annalisa,Borgogno-Mondino, Enrico,et al. Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)[J],2024,13(1).
APA	Orusa, Tommaso,Viani, Annalisa,Borgogno-Mondino, Enrico,&Suziedelyte-Visockiene, Jurate.(2024).Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy).LAND,13(1).
MLA	Orusa, Tommaso,et al."Earth Observation Data and Geospatial Deep Learning AI to Assign Contributions to European Municipalities Sen4MUN: An Empirical Application in Aosta Valley (NW Italy)".LAND 13.1(2024).