DEEP LEARNING-BASED LAND USE LAND COVER SEGMENTATION OF HISTORICAL AERIAL IMAGES

被引：1

作者：

Sertel, Elif ^{[1
]}

Avci, Cengiz ^{[1
,2
]}

Kabadayi, Mustafa Erdem ^{[2
]}

机构：

[1] Istanbul Tech Univ, Dept Geomat Engn, TR-34469 Maslak, Turkiye

[2] Koc Univ, Dept Hist, TR-34450 Istanbul, Turkiye

来源：

IGARSS 2023 - 2023 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2023年

基金：

欧洲研究理事会;

关键词：

LULC; historical aerial photographs; deep learning; segmentation;

D O I：

10.1109/IGARSS52108.2023.10281819

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

This study aims to generate a new benchmark dataset from historical panchromatic aerial photographs suitable for deep learning-based Land use/Land cover (LULC) segmentation task. This new benchmark dataset spans a wide geographic area and consists of aerial photographs from various populous areas in Turkey and Bulgaria from the 1950s, 1960s, and 1970s. We implemented U-Net++ and Deeplabv3 segmentation architectures and appropriate hyperparameters and backbone structures to determine the applicability of this dataset, specifically for accurate and fast mapping of past terrain conditions. This unique historical LULC dataset and the different combinations of deep learning experiments proposed can be applied to different geographical regions with similar panchromatic datasets.

引用

页码：2622 / 2625

页数：4

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