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In the capitalist rush to attract more visitors, cities are committing significant resources to heritage conservation, driven by the substantial economic benefits generated by the tourism industry. However, less famous or less well-resourced cities, often with smaller populations, also known as intermediary cities, find it difficult to allocate funds to protect their most significant heritage sites. In this conservation context, intermediary cities, often on the periphery or 'at the margins', can fill the gaps and needs of urbanism through a better strategic understanding of the challenges of global touristification, thus this research provides urban planning tools for local governments with limited resources to preserve their architectural heritage through remote sensing, for its advantages in terms of lower economic cost, as a valuable monitoring tool to effectively identify high-vulnerability sites that require priority attention in the conservation of architectural heritage. In other words, it allows for a reduction in the territory of those areas located 'at the margins' in terms of urban planning and management, by approaching the territorial, urban, architectural and tourism problems from a transdisciplinary perspective in the preservation of the architectural heritage. This study explores the application of optical (Sentinel-2) using neural networks for classifying the land cover and radar (Sentinel-1 and PAZ) satellite images to obtain the ground motion as a geotechnical risk study, together with geospatial data, for the monitoring of architectural heritage in intermediate cities. Focusing on the districts of Bragança and Guarda in Portugal, the approach allows the direct identification of vulnerable architectural heritage, identifying 9 highly-vulnerable areas using PAZ data and 7 areas using Sentinel-1 data. Furthermore, this work provides an understanding of the potential and limitations of these technologies in heritage preservation because compares the processing results of freely accessible medium-resolution Sentinel-1 radar imagery with the high-resolution radar images from the innovative PAZ satellite. [ABSTRACT FROM AUTHOR]

Atmospheric delay (AD) is the main source of error in time-series interferometric synthetic aperture radar (InSAR) deformation estimation over large areas. In this study, we propose a bidirectional gated recurrent unit (BiGRU) model to correct random and seasonal ADs adaptively. The BiGRU model decomposes InSAR time-series measurements into non-seasonal and seasonal components by adopting a branched network structure and extracting component-wise features separately. To remove seasonal ADs and meanwhile preserve true seasonal deformation, a dense and fully connected layer with weighted feature learning was designed. Five typical time-series deformation patterns were simulated for model training, and its robustness was evaluated using synthetic data. We applied the trained model to two city clusters in China (Guangdong and Jiangxi-Hunan) using 178 Sentinle-1 images. The results showed that BiGRU with moderate Generic Atmospheric Correction Online Service (GACOS) and spatiotemporal filtering (pGA_Fi_BiGRU) reduced the standard deviation of InSAR time-series measurements by 64.3% in the Guangdong region and by 53.5% in the Jiangxi-Hunan region compared with the raw data. Compared with the traditional combined GACOS and spatiotemporal filtering processing methods, the pGA_Fi_BiGRU improved the AD reduction performance by 4.7% and by 8.5% in Guangdong and Jiangxi-Hunan, respectively. The InSAR time-series deformation after pGA_Fi_BiGRU processing removed residual ADs and preserved true deformation, which agreed well with the geodetic leveling and Global Navigation Satellite System data. The first overall subsidence velocity of the Irrawaddy Delta city cluster in Myanmar was then mapped, followed by time-series deformation estimation using pGA_Fi_BiGRU. Representative time-series deformation due to groundwater extraction, coastal erosion, and accretion were properly derived, suggesting that the proposed model can be generalized to other city clusters with different atmospheric noise and geophysical dynamics. • Land subsidence of two city clusters was measured using Sentinel-1 images. • The pGA_Fi_BiGRU model was proposed to correct the seasonal ADs. • The performance enhancement in AD reduction varied across different cities. • Generalizability was demonstrated using the third city cluster. [ABSTRACT FROM AUTHOR]