Mapping of impervious surfaces with the use of remote sensing imagery: Support Vector Machines classification and GIS-based approach

• Autorzy: Sobieraj Janusz , Marín Marcos Fernández , Metelski Dominik

Identyfikatory

DOI

10.24425/ace.2023.146071

Warianty tytułu

PL

Wizualizacja powierzchni nieprzepuszczalnych z wykorzystaniem zdjęć teledetekcyjnych: klasyfikacja support vector machines i podejście oparte na GIS

• Języki publikacji: EN

Abstrakty

EN

This study focuses on the problem of mapping impervious surfaces in urban areas and aims to use remote sensing data and orthophotos to accurately classify and map these surfaces. Impervious surface indices and green space assessments are widely used in land use and urban planning to evaluate the urban environment. Local governments also rely on impervious surface mapping to calculate stormwater fees and effectively manage stormwater runoff. However, accurately determining the size of impervious surfaces is a significant challenge. This study proposes the use of the Support Vector Machines (SVM) method, a pattern recognition approach that is increasingly used in solving engineering problems, to classify impervious surfaces. The research results demonstrate the effectiveness of the SVM method in accurately estimating impervious surfaces, as evidenced by a high overall accuracy of over 90% (indicated by the Cohen’s Kappa coefficient). A case study of the “Parkowo-Leśne” housing estate in Warsaw, which covers an area of 200,000 m², shows the successful application of the method. In practice, the remote sensing imagery and SVM method allowed accurate calculation of the area of the surface classes studied. The permeable surface represented about 67.4% of the total complex and the impervious surface corresponded to the remaining 32.6%. These results have implications for stormwater management, pollutant control, flood control, emergency management, and the establishment of stormwater fees for individual properties. The use of remote sensing data and the SVM method provides a valuable approach for mapping impervious surfaces and improving urban land use management.

PL

Niniejsze badanie koncentruje się na problemie wyznaczania powierzchni nieprzepuszczalnych na obszarach miejskich i ma na celu wykorzystanie danych teledetekcyjnych i ortofotomap do dokładnej klasyfikacji i wizualizacji tych powierzchni. Wskaźniki powierzchni nieprzepuszczalnych i oceny terenów zielonych są szeroko stosowane w planowaniu przestrzennym i urbanistycznym do oceny środowiska miejskiego. Władze lokalne polegają również na oszacowaniu wielkości powierzchni nieprzepuszczalnych w celu obliczania opłat za wodę deszczową i skutecznego zarządzania odpływem wody deszczowej. Jednak dokładne określenie wielkości nieprzepuszczalnych powierzchni jest poważnym wyzwaniem. W niniejszym badaniu zaproponowano wykorzystanie metody Support Vector Machines (SVM), podejścia opartego na rozpoznawaniu wzorców, które jest coraz częściej stosowane w rozwiązywaniu problemów inżynieryjnych, do klasyfikacji powierzchni nieprzepuszczalnych. Wyniki badań pokazują skuteczność metody SVM w dokładnym szacowaniu powierzchni nieprzepuszczalnych, o czym świadczy wysoka ogólna precyzja wynosząca ponad 90% (na co wskazuje współczynnik Kappa Cohena). Studium przypadku osiedla „Parkowo-Leśne” w Warszawie o powierzchni 200 000 m² pokazuje skuteczne zastosowanie metody. Wyniki wskazują, że powierzchnie przepuszczalne stanowiły około 67,4% całego kompleksu, podczas gdy powierzchnie nieprzepuszczalne stanowiły pozostałe 32,6%. Wyniki te mogą mieć wpływ na zarządzanie wodami opadowymi, kontrolę zanieczyszczeń, zapobieganie powodziom, zarządzanie kryzysowe i ustalanie opłat za wodę opadową dla poszczególnych nieruchomości. Wykorzystanie danych teledetekcyjnych i metody SVM zapewnia cenne podejście do wizualizacji powierzchni nieprzepuszczalnych i poprawy zarządzania użytkowaniem gruntów miejskich.

Słowa kluczowe

EN

classification; impervious surface; support vector machine; remote sensing; geographic information system; land use land cover; ArcGIS; machine learning

PL

klasyfikacja; powierzchnia nieprzepuszczalna; maszyna wektorów nośnych; teledetekcja; system informacji geograficznej; użytkowanie gruntów pokrycie gruntów; ArcGIS; uczenie maszynowe

• Wydawca: Komitet Inżynierii Lądowej i Wodnej PAN ; Politechnika Warszawska, Wydział Inżynierii Lądowej
• Czasopismo: Archives of Civil Engineering
• Rocznik: 2023
• Tom: Vol. 69, nr 3
• Strony: 129--146
• Opis fizyczny: Bibliogr. 42 poz., il., tab.

Twórcy

autor

Sobieraj Janusz

• Warsaw University of Technology, Faculty of Civil Engineering, Warsaw, Poland

• https://orcid.org/0000-0002-0819-7384

autor

Marín Marcos Fernández

• Universitat Politècnica de València, Department of Computer Science and Artificial Intelligence, Paterna (Valencia), Spain

• https://orcid.org/0000-0002-0307-0392

autor

Metelski Dominik

• University of Granada, Faculty of Economics and Business Sciences, Campus Cartuja, Granada, Spain

• https://orcid.org/0000-0003-0195-0342

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