Neural Network-Based Urban Change Monitoring with Deep-Temporal Multispectral and SAR Remote Sensing Data

Abstract

Remote-sensing-driven urban change detection has been studied in many ways for decades for a wide field of applications, such as understanding socio-economic impacts, identifying new settlements, or analyzing trends of urban sprawl. Such kinds of analyses are usually carried out manually by selecting high-quality samples that binds them to small-scale scenarios, either temporarily limited or with low spatial or temporal resolution. We propose a fully automated method that uses a large amount of available remote sensing observations for a selected period without the need to manually select samples. This enables continuous urban monitoring in a fully automated process. Furthermore, we combine multispectral optical and synthetic aperture radar (SAR) data from two eras as two mission pairs with synthetic labeling to train a neural network for detecting urban changes and activities. As pairs, we consider European Remote Sensing (ERS-1/2) and Landsat 5 Thematic Mapper (TM) for 1991–2011 and Sentinel 1 and 2 for 2017–2021. For every era, we use three different urban sites—Limassol, Rotterdam, and Liège—with at least 500km2 each, and deep observation time series with hundreds and up to over a thousand of samples. These sites were selected to represent different challenges in training a common neural network due to atmospheric effects, different geographies, and observation coverage. We train one model for each of the two eras using synthetic but noisy labels, which are created automatically by combining state-of-the-art methods, without the availability of existing ground truth data. To combine the benefit of both remote sensing types, the network models are ensembles of optical- and SAR-specialized sub-networks. We study the sensitivity of urban and impervious changes and the contribution of optical and SAR data to the overall solution. Our implementation and trained models are available publicly to enable others to utilize fully automated continuous urban monitoring.

Assessing Human Settlement Sprawl in Mexico via Remote Sensing and Deep Learning

“…While some methods still rely on classical techniques, such as SVM and RF [13], [18], our approach follows the recent trend of employing deep learning techniques. However, in contrast to some recent studies [24], [25] that employ techniques with multiple remote sensing sources such as multispectral images, range, and radar, our approach uses a large dataset of multispectral images. Our approach discriminates between inhabited and uninhabited classes without aiming to refine inferences into subclasses such as houses, roads, and buildings [16] and our architecture to semantically segment the area under analysis [23] provided limited results.…”

“…For example, Wu et al [23] used a semantic segmentation U-Net for built-up area detection from SAR images. Zitzlsberger et al [24] processed multispectral and SAR images to monitor urban change using a CNN and an RNN. Fibaek et al [25] detected human structures using a CNN with range, optical, and radar observations.…”