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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.

The Sentinel-1 satellite system continuously observes European countries at a relatively high revisit frequency of six days per orbital track. Given the Sentinel-1 configuration, most areas in Czechia are observed every 1–2 days by different tracks in a moderate resolution. This is attractive for various types of analyses by various research groups. The starting point for interferometric (InSAR) processing is an original data provided in a Single Look Complex (SLC) level. This work represents advantages of storing data augmented to a specifically corrected level of data, SLC-C. The presented database contains Czech nationwide Sentinel-1 data stored in burst units that have been pre-processed to the state of a consistent well-coregistered dataset of SLC-C. These are resampled SLC data with their phase values reduced by a topographic phase signature, ready for fast interferometric analyses (an interferogram is generated by a complex conjugate between two stored SLC-C files). The data can be used directly into multitemporal interferometry techniques, e.g., Persistent Scatterers (PS) or Small Baseline (SB) techniques applied here. A further development of the nationwide system utilising SLC-C data would lead into a dynamic state where every new pre-processed burst triggers a processing update to detect unexpected changes from InSAR time series and therefore provides a signal for early warning against a potential dangerous displacement, e.g., a landslide, instability of an engineering structure or a formation of a sinkhole. An update of the processing chain would also allow use of cross-polarised Sentinel-1 data, needed for polarimetric analyses. The current system is running at a national supercomputing centre IT4Innovations in interconnection to the Czech Copernicus Collaborative Ground Segment (CESNET), providing fast on-demand InSAR results over Czech territories. A full nationwide PS processing using data over Czechia was performed in 2017, discovering several areas of land deformation. Its downsampled version and basic findings are demonstrated within the article., This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science—LQ1602” and from the Large Infrastructures for Research, Experimental Development and Innovations project “IT4Innovations National Supercomputing Center—LM2015070”.

Space-borne Synthetic Aperture Radar (SAR) Interferometry (InSAR) is now a key geophysical tool for surface deformation studies. The European Commission’s Sentinel-1 Constellation began acquiring data systematically in late 2014. The data, which are free and open access, have global coverage at moderate resolution with a 6 or 12-day revisit, enabling researchers to investigate large- scale surface deformation systematically through time. However, full exploitation of the potential of Sentinel-1 requires specific processing approaches as well as the efficient use of modern computing and data storage facilities. Here we present LiCSAR, an operational system built for large-scale interferometric processing of Sentinel-1 data. LiCSAR is designed to automatically produce geocoded wrapped and unwrapped interferograms and coherence estimates, for large regions, at 0.001° resolution (WGS-84 system). The products are continuously updated in a frequency depending on prioritised regions (monthly, weekly or live update strategy). The products are open and freely accessible and downloadable through an online portal. We describe the algorithms, processing, and storage solutions implemented in LiCSAR, and show several case studies that use LiCSAR products to measure tectonic and volcanic deformation. We aim to accelerate the uptake of InSAR data by researchers as well as non-expert users by mass producing interferograms and derived products., This work was partially undertaken on ARC4, part of the High Performance Computing facilities at the University of Leeds, UK.

Our automatized interferometric monitoring system, IT4S1, contains a database of Sentinel-1 satellite image bursts that have been preprocessed to the state of a consistent well-coregistered dataset. The coregistration solution introduces a new type of data, an SLC-C (corrected single look complex data). These are SLC images ready for interferometric analyses with maximally reduced phase signature of topography and flat-Earth phase. The further processing time for multitemporal interferometry techniques is significantly reduced for achieving velocity maps based on e.g. Persistent Scatterers or Small Baseline techniques. In this paper, authors focus on quality of estimated topographic phase, finding out that computation of perpendicular baseline between satellite positions based on precise orbit ephemerides can differ and thus the approach may suffer from a residual topographic signature or a flat-Earth phase. The paper presents conclusions taken by an investigation of one such pre-prepared dataset over a mountainous area in southern Spain. Though some orbital inparallelity occurs during Sentinel-1 acquisitions (causing inaccuracies of perpendicular baselines within first meters), the flat-Earth is removed properly using this approach. The paper further demonstrates results using SLC-C approach using available and custom processing techniques and notifies about the possibility that in terms of overall accuracy and precision of multitemporal interferometry this approach contains inaccuracies that can be either estimated as part of error phase screens or even neglected.

After two decades of SAR satellite operation, their data, processed by interferometric techniques (InSAR), allows for a sensitive detection of terrain movements. Various projects have risen towards the detection of landslide activity and thus possibility of early warning system based on SAR data. The ability of InSAR techniques to detect a slope stability is limited and depends on slope orientation, movement rate, vegetation cover and temporal and spatial image resolution. This paper presents common issues found in past projects of the team of authors observing landslides using available satellite SAR data processed by various InSAR techniques. It is demonstrated that in some cases the basic differential InSAR combining only two images provides more valuable information compared to advance multi-temporal InSAR techniques. The paper aims at providing guidelines for maximal successful detection of landslide activity and to introduce pros and cons of the InSAR method, which often discussed in geology and geophysics circles. We prove the potential of techniques using satellite-based InSAR to identify creeping movement of structures built at moving slopes and the limited possibility to detect movements at moderately vegetated slopes by using especially the L-band or a combination of winter-season SAR and other images within very short temporal difference. It is possible to overcome misinterpretation of results by proper understanding of InSAR source phase component at slopes, including phase distortions due to SAR geometry, vegetation movement (vegetated mass movement) and atmospheric pressure changes correlated with height differences.

The topic of this work covers current implementation of satellite radar (SAR) interferometry (InSAR) techniques for routine identification of dynamic land processes such as downhill creep and landslide activity, subsidence or displacements of various objects of infrastructure. With the emerge of European Copernicus programme, the need of effectivity in satellite Big Data processing increased. There are two Sentinel-1 satellites observing the Earth with 6 days revisit time, sending daily 100 TB of data to be archived. In case of the relatively small area of Czechia, the amount of data to be archived in a Czech national mirror is around 24 GB per day. Czech CESNET e-infrastructure has accepted the role of assessing Copernicus Ground Segment programme. A database mirroring Sentinel data over Czechia is established, however still in its early stage. A potential service based on an interferometric processing of Sentinel-1 data from this database has been prepared in Czech national supercomputing center IT4Innovations. As a basis of the system, several open-source projects were deployed, including MySQL-based burst metadatabase (TU Leeds), ISCE TOPS Processor (NASA/JPL), Doris coregistration algorithm (TU Delft) and StaMPS Small Baselines processor (Stanford University). Though more functionality can be rapidly developed, incorporating some of the own post-processing algorithms, even current early version of the system can yield interesting results by a fully automatic processing chain.

This short paper summarizes the effects of DEM errors influencing precision of deformation monitoring results using satellite SAR interferometry and presents basic mathematical approaches to quantify DEM accuracy needed for proper estimation of SAR interferometry parameters. While in case of appropriately large SAR dataset for Multi-Temporal Interferometry (MTI) processing the height difference is usually well estimated, still DEM accuracy should be carefully considered in case of using low number of images—and especially in case of processing differential InSAR with only one image pair. Depending on the interferogram pair combination characteristics, especially parameter known as perpendicular baseline (Bperp) describing the distance between satellite positions during their image acquisitions, the interferogram is more or less prone to DEM accuracy errors. In case of higher Bperp, a relatively high quality DEM is necessary to remove stereoscopic effect apparent in interferogram in order to distinguish interferogram phase element caused by temporal terrain deformation.

Part of Czech highway D1 connecting Ostrava city with Prague and Poland, is built over an undermined area of Ostrava-Svinov, with mines closed since 1991 in the area. Soon after its opening ceremony in 2008, the highway began to exhibit various significant deformations. An attempt of InSAR monitoring using ERS, Envisat and several TerraSAR-X Spotlight acquisitions was done in order to monitor progress of deformations, to consider suitability of satellite InSAR for such purposes and to search for footprints whether the deformations were caused by fading subsidence due to undermining or by another, local, reason. Only shallow subsidence was found in the area from Envisat data ranging 2005-2010. Highway deformations in late 2011 are observable from processing of limited set of 5 TerraSAR-X images using different methods. Detected deformations are very probably due to longitudinal thermal expansion of the observed highway bridge. This publication contains issues to be taken into consideration for appropriate interpretation and processing if the available input dataset is limited and not optimal.