BEGIN:VCALENDAR VERSION:2.0 PRODID:-//pretalx//talks.staging.osgeo.org//foss4g-it-2023//speaker//WUBJHD BEGIN:VTIMEZONE TZID:GMT BEGIN:STANDARD DTSTART:20001029T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZNAME:GMT TZOFFSETFROM:+0100 TZOFFSETTO:+0000 END:STANDARD BEGIN:DAYLIGHT DTSTART:20000326T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZNAME:BST TZOFFSETFROM:+0000 TZOFFSETTO:+0100 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT UID:pretalx-foss4g-it-2023-899M3E@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T144500 DTEND;TZID=GMT:20230612T150000 DESCRIPTION:Authors: Claudio Ladisa\, Manuel A. Aguilar\, Alessandra Capolu po\, Eufemia Tarantino\, Fernando J. Aguilar.\nThe use of renewable energy sources in power generation is increasing due to environmental awareness and technological advancements. Solar energy\, with its extensive availabi lity and minimal greenhouse gas emissions\, is a promising source. However \, large photovoltaic (PV) plants require constant monitoring to ensure ef ficiency and reliability. Remote sensing technology can be beneficial in p roviding accurate information on the plant's size\, shape\, and location\, reducing costs and increasing monitoring efficiency. The detection of lar ge PV plants can be carried out using various technologies\, including the use of satellite imagery\, drones imagery or observation from aircraft. H owever\, the use of satellite imagery is advantageous for the detection of large PV plants because it allows to acquire data on a large area without having to move around the site and to monitor the plant over time without interfering with its activity. Open-source imagery from satellites like S entinel-2 (S2) and Landsat 9 has led to a significant increase in remote s ensing research related to extracting (PV) systems. This is because the fr ee and public availability of high-quality images with extensive spatial c overage has eliminated the need to buy costly private satellite images. Ad ditionally\, the frequency of image acquisition\, which can occur every fe w days\, has allowed for quick and accurate monitoring of areas of interes t. Several research have recently merged remote sensing with machine learn ing (ML) methods to develop automatic classification algorithms for PV sys tems. Most of these algorithms employ different spectral indices\, such as the Normalized Difference Water Index (NDWI)\, the Normalized Difference Vegetation Index (NDVI)\, and Normalized Difference Bare Index (NDBI)\, as input. These spectral indices provide useful information on the presence of water\, vegetation and bare soil\, respectively\, which can be used to identify PV systems more accurately\, thus improving classification accura cy. However\, there is no specific spectral index that has been tested exc lusively for the extraction of PV. This is partially because PV arrays may be constructed on many kinds of surfaces\, in various environmental and c limatic circumstances\, and with different solar panel sizes and types. In this regard\, the goal of this work was to suggest a Photovoltaic Systems Extraction Index (PVSEI) for the detection of PV installations from S2 im ages in two distinct study areas characterized by the persistent presence of large PV installations: The province of Viterbo (Italy) and the provinc e of Seville (Spain). The development of the PVSEI was based on the combin ation of different bands provided by S2\, in order to maximise the spectra l difference between the solar panels and their surroundings. For each stu dy area\, two S2 images\, one taken in February and the other one in Augus t\, were used to analyse the seasonal variation of the solar panels' spect ral signature and test the PVSEI's accuracy in each of the four scenarios. The image analysis was carried out using an Object-Based Image Analysis ( OBIA) method since it allowed for a more accurate identification of PV sys tems than the pixel-based method\, which analyzes individual elements with out taking their spatial arrangement and semantic significance into accoun t. Multi-resolution segmentation was used to create segments with differen t dimensions based on scale\, shape and compactness parameters. The Decisi on Tree (DT) classifier was used to evaluate the effectiveness of the PVSE I and its importance in comparison to the other indices used in the litera ture in both locations and for both periods after the objects had been lab elled as "PV" and "No-PV”. The effectiveness of the new index was demons trated through the results obtained from the DT analysis. In three out of four scenarios\, the PVSEI was selected as the first cut in the DT analysi s. In the remaining scenario where it was not ranked first\, it still main tained a high level of significance\, being the second index in importance . The accuracy was assessed using an error matrix calculated on both the e ntire segmentation dataset (i.e. using all the objects) and with TTA mask with 2 m pixel size. Four metrics were used to evaluate accuracy of the PV SEI\, including Overall Accuracy (OA)\, Kappa Index of Agreement (KIA)\, P roducer Accuracy (PA)\, and User Accuracy (UA) for both classes. OA exceed ed 98% in all scenarios\, both for the segmentation dataset and the TTA ma sk. KIA values for the TTA mask ranged from 0.81 to 0.86\, while values fo r the segmentation objects ranged from 0.74 to 0.82. In conclusion\, the n ew index has demonstrated favourable outcomes in both study areas\, with o nly a limited number of misclassifications involving bare soil objects tha t have a spectral signature resembling certain photovoltaic systems. DTSTAMP:20260517T205252Z LOCATION:Sala Videoconferenza @ PoliBa SUMMARY:Development of a Photovoltaic System Extraction Index for the detec tion of large PV plants using Sentinel-2 images - Alessandra Capolupo\, Eu femia Tarantino\, Claudio Ladisa\, Fernando J. Aguilar URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/899M3E/ END:VEVENT BEGIN:VEVENT UID:pretalx-foss4g-it-2023-KJBN9T@talks.staging.osgeo.org DTSTART;TZID=GMT:20230612T154500 DTEND;TZID=GMT:20230612T160000 DESCRIPTION:Authors: A. Capolupo & E. Tarantino\nSeveral research involving Earth's physical processes and depicting environmental systems are comput ationally time-consuming\, and as a result\, have a substantial impact on the time necessary to collect and manage the data. Over the years\, numero us acceptable methods for describing surface morphology and enabling quick computer solutions were developed. Nevertheless\, since 1991\, Digital El evation Model (DEM) has been recognized as the finest alternative for atta ining this goal because\, in addition to its capacity to provide baseline morphological information quickly\, it also has the exclusive property of being a 2.5-D surface. The quality and trustworthiness of the results prov ided by its use are determined by its resolution\, elevation accuracy\, an d shape/topological correctness. Elevation accuracy is normally establishe d by statistically analysing differences between DEMs and reference datase ts such as Ground Control Points (GCPs)\, whereas shape/topological correc tness is typically defined by demonstrating DEM conformity with some unive rsal principles. Therefore\, the root mean square error is commonly used t o achieve the first aim\, whilst DEM derivates are examined in the second one. However\, neither approach is without limits since their performance is influenced by the quality of the reference data and the complexity in m easuring DEM realism.\nThis is much more difficult when the DEM under cons ideration encompasses the entire globe. Even though they are described as a homogenous product\, the accuracy of Global DEMs in terms of elevation a nd realism varies according to geographical location and morphology\, land cover\, and climate. Furthermore\, as satellite stereoscopic technologies \, as well as photogrammetric and SAR interferometric methods\, have evolv ed\, the amount of Global DEMs collected has substantially increased. Most of them were also collected in different historical periods and\, consequ ently\, they may be useful free open-source data for conducting a consiste nt global study change detection analysis.\nIn such a framework\, this stu dy aims to investigate the appropriateness of medium-resolution open-acces s Global DEMs in evaluating changes in urban contexts between 2000 and 201 1. To accomplish this\, the primary freely accessible Global DEMs were sta tistically examined\, and after selecting the best pair\, a change detecti on analysis was carried out. To assess its accuracy\, the findings were co mpared to the Copernicus Land Monitoring service's land use layers from th e same historical periods (https://land.copernicus.eu/). Lastly\, this stu dy seeks to estimate and predict the caused by building density bias in ac cordance with the urban fabric type.\nThe procedure was implemented by wri ting appropriate Java-script code on the Google Earth Engine (GEE) web-bas ed platform. Hence\, the GEE catalogue was first consulted to determine th e available Global DEMs corresponding to the historical period under inves tigation\, and\, once identified\, they were imported into the application programming interface and validated using the "internal" technique. As a result\, AW3D30 (3.2)\, which was launched in early January 2021\, and SRT M DEM V3 were deemed the optimal combination for research purposes during an 11-year timeframe. Thus\, they were used as input data for calculating the corresponding DEM of Differences (DoD) and quantify the alteration in urban environments. Owing to the law propagation error\, the resultant DoD had substantial internal incoherencies\, which were subsequently statisti cally eliminated by using the Tukeys' filter. This is widely acknowledged as an effective method for identifying and cleaning out internal noise wit hout prior awareness of it. Yet\, a significant amount of Tukey's outliers was identified and eliminated in their respective DoD\, mostly in wooded and hilly zones\, owing to differing degrees of quality of the input data. Following that\, to reduce misclassification and distinguish noise from r eal changes\, the resulting DoD was further filtered using the Uniformly D istributed Error (UDE) strategy\, developed by Brasington et al. in 2003. However\, the UDE technique\, while exploiting a gaussian distribution of internal error\, does not adapt the filtering threshold to the local condi tions\, resulting in an over or underestimation of the amount of informati on to remove. Urban variation was now assessed by combining the filtered D oD result with Corine Land Cover (CLC) data. This integration also enabled statistical investigation and modelling of the DoD error associated with urban fabric type. When comparing the CLC information to both Tukey's outl iers and UDE noise in urban areas\, it is discovered that error increased linearly with building density. This implies that urban changes quantifica tion could be improved further by correcting the building density bias. In future works\, the introduced approach will be enhanced by taking buildin g height into consideration. DTSTAMP:20260517T205252Z LOCATION:Sala Biblioteca @ PoliBa SUMMARY:Estimating the influence of building density bias on the accuracy o f Global DEM of Differences in urban change analysis - Alessandra Capolupo \, Eufemia Tarantino URL:https://talks.staging.osgeo.org/foss4g-it-2023/talk/KJBN9T/ END:VEVENT END:VCALENDAR