Land subsidence in urban environments is an increasingly prominent aspect in the monitoring and maintenance of urban infrastructures. In this study we update the subsidence information over Rome and its surroundings (already the subject of past research with other sensors) for the first time using Copernicus Sentinel-1 data and open source tools. With this aim, we have developed a fully automatic processing chain for land deformation monitoring using the European Space Agency (ESA) SentiNel Application Platform (SNAP) and Stanford Method for Persistent Scatterers (StaMPS). We have applied this automatic processing chain to more than 160 Sentinel-1A images over ascending and descending orbits to depict primarily the Line-Of-Sight ground deformation rates. Results of both geometries were then combined to compute the actual vertical motion component, which resulted in more than 2 million point targets, over their common area. Deformation measurements are in agreement with past studies over the city of Rome, identifying main subsidence areas in: (i) Fiumicino; (ii) along the Tiber River; (iii) Ostia and coastal area; (iv) Ostiense quarter; and (v) Tivoli area. Finally, post-processing of Persistent Scatterer Inteferometry (PSI) results, in a Geographical Information System (GIS) environment, for the extraction of ground displacements on urban infrastructures (including road networks, buildings and bridges) is considered.

Buried archaeological structures, such as earthworks and buildings, often leave traces at the surface by altering the properties of overlying material, such as soil and vegetation. These traces may be better visible from a remote perspective than on the surface. Active and passive airborne and spaceborne sensors acquiring imagery from the ultraviolet to infrared have been shown to reveal these archaeological residues following the application of various processing techniques. While the active microwave region of the spectrum, in the form of Synthetic Aperture Radar (SAR) has been used for archaeological prospection, particularly in desert regions, it has yet to be fully exploited to detect buried structures indirectly though proxy indicators in overlying materials in vegetated areas. Studies so far have tended to focus on the intensity of the SAR signal, without making full use of the phase. This paper demonstrates that SAR backscatter intensity, coherence and interferometry can be used to identify archaeological residues over a number of areas in the vicinity of Rome, Italy. SAR imagery from the COnstellation of small Satellites for the Mediterranean basin Observation (COSMO-SkyMed) have been obtained for the analysis: 77 scenes in Stripmap and 27 in Spotlight mode. Processing included multitemporal speckle filtering, coherence generation and Digital Elevation Model (DEM) creation from Small Baseline Subsets (SBAS). Comparison of these datasets with archaeological, geological, soil, vegetation and meteorological data reveal that several products derived from SAR data can expose various types of archaeological residues under different environmental conditions.

Cost-effective techniques for systematic archaeological prospection are essential to improve the efficiency of preventive archaeology and the preservation of cultural heritage. Web Mapping Services, such as Microsoft Bing Maps, provide imagery covering extensive areas at high resolution. These can, in some cases, reveal cropmarks of buried historical structures. Given that archaeological prospection is not generally the priority of most common Web Mapping Services, the conditions under which images are acquired are not always suitable for the appearance of cropmarks. Therefore, their detection is typically serendipitous. This pilot project attempts to assess the potential to use the Microsoft Bing Maps Bird’s Eye service within a crowdsourcing platform to systematically search for archaeological cropmarks in the surroundings of the city of Rome in Italy. On this platform, which is hosted by the company Scifabric (Southampton, UK) and based on PyBossa, an Open Source framework for crowdsourcing, members of the public are invited to interpret oblique air photo tiles of Bing Maps Bird’s Eye. While the project is still on-going, at least one seamless coverage of tiles in the area of interest has been interpreted. For each tile, the Bing Maps Bird’s Eye service provides oblique air photo coverage in up to four possible orientations. As of 5 July 2020, 18,765 of the total 67,014 tasks have been completed. Amongst these completed tasks, positive detections of cropmarks were recorded once for 1447 tasks, twice for 57 tasks, and three or more times for 10 tasks. While many of these detections may be erroneous, some correspond with archaeological cropmarks of buried remains of buildings, roads, aqueducts, and urban areas from the Roman period, as verified by comparison with archaeological survey data. This leads to the conclusion that the Bing Maps Bird’s Eye service contains a wealth of information useful for archaeological prospection, and that to a certain extent citizen researchers could help to mine this information. However, a more thorough analysis would need to be carried out on possible false negatives and biases related to the varying ease of interpretation of residues of different archaeological structures from multiple historical periods. This activity forms the first part of a research project on the systematic prospection of archaeological cropmarks. The ultimate aim is to reach a critical mass of training data through crowdsourcing which can be augmented and used as input to train a machine learning algorithm for automatic detection on a larger scale.

North Sinai is of significant historical interest primarily because of its role since late prehistoric times as a land bridge between Egypt and the Levant. Access to this region is challenging due to its harsh geography and security concerns. Remote sensing constitutes a convenient method for archaeological prospection and monitoring over such regions with its low cost (relative to ground based sensing techniques), global coverage, and high temporal and spatial sampling. This paper describes part of a study to revisit a number of sites investigated during the North Sinai Survey (1972–1982) with very high resolution optical and Synthetic Aperture Radar satellite imagery. These were acquired throughout the summer of 2017 in the framework of a European Space Agency research project. The Synthetic Aperture Radar data includes Spotlight and Staring Spotlight modes of the TerraSAR-X mission, while the optical imagery was acquired by the Pleiades mission. The TerraSAR-X data were processed to derive filtered amplitude and consecutive coherence time series. The results of the TerraSAR-X data processing, and the pan-sharpened Pleiades data were compared with the results of the North Sinai Survey to detect possible additional buried structures in the radar data, or newly excavated sites in the optical data. While the analysis is still ongoing, results are reported here of the Qasrawet archaeological site, which was partially investigated by the North Sinai Survey expedition, but assumed to cover a much larger area. Herein, a number of newly excavated structures are apparent in the remote sensing data. The similarity of features in both the TerraSAR-X and Pleiades data suggest that all structures are surface residues, and therefore, that the subsurface mapping capabilities of the TerraSAR-X data in this area are limited. The utility of both data types for archaeological site monitoring are discussed.

Techniques were implemented to extract anthropogenic features in the desert region of North Sinai using data from the first- and second-generation Phased Array type L-band Synthetic Aperture Radar (PALSAR-1 and 2). To obtain a synoptic view over the study area, a mosaic of average, multitemporal (De Grandi) filtered PALSAR-1 σ° backscatter of North Sinai was produced. Two subset regions were selected for further analysis. The first included an area of abundant linear features of high relative backscatter in a strategic, but sparsely developed area between the Wadi Tumilat and Gebel Maghara. The second included an area of low backscatter anomaly features in a coastal sabkha around the archaeological sites of Tell el-Farama, Tell el-Mahzan, and Tell el-Kanais. Over the subset region between the Wadi Tumilat and Gebel Maghara, algorithms were developed to extract linear features and convert them to vector format to facilitate interpretation. The algorithms were based on mathematical morphology, but to distinguish apparent man-made features from sand dune ridges, several techniques were applied. The first technique took as input the average σ° backscatter and used a Digital Elevation Model (DEM) derived Local Incidence Angle (LAI) mask to exclude sand dune ridges. The second technique, which proved more effective, used the average interferometric coherence as input. Extracted features were compared with other available information layers and in some cases revealed partially buried roads. Over the coastal subset region a time series of PALSAR-2 spotlight data were processed. The coefficient of variation (CoV) of De Grandi filtered imagery clearly revealed anomaly features of low CoV. These were compared with the results of an archaeological field walking survey carried out previously. The features generally correspond with isolated areas identified in the field survey as having a higher density of archaeological finds, and interpreted as possible islands of dry land, which may have been surrounded by lagoons, rivers, and swamplands in antiquity. It is suggested that these surrounding areas may still have a higher water content, sufficient to be detected in processed Synthetic Aperture Radar (SAR) imagery.

The availability of free and open data from Earth observation programmes such as Copernicus, and from collaborative projects such as Open Street Map (OSM), enables low cost artificial intelligence (AI) based monitoring applications. This creates opportunities, particularly in developing countries with scarce economic resources, for large–scale monitoring in remote regions. A significant portion of Earth’s surface comprises desert dune fields, where shifting sand affects infrastructure and hinders movement. A robust, cost–effective and scalable methodology is proposed for road detection and monitoring in regions covered by desert sand. The technique uses Copernicus Sentinel–1 synthetic aperture radar (SAR) satellite data as an input to a deep learning model based on the U–Net architecture for image segmentation. OSM data is used for model training. The method comprises two steps: The first involves processing time series of Sentinel–1 SAR interferometric wide swath (IW) acquisitions in the same geometry to produce multitemporal backscatter and coherence averages. These are divided into patches and matched with masks of OSM roads to form the training data, the quantity of which is increased through data augmentation. The second step includes the U–Net deep learning workflow. The methodology has been applied to three different dune fields in Africa and Asia. A performance evaluation through the calculation of the Jaccard similarity coefficient was carried out for each area, and ranges from 84% to 89% for the best available input. The rank distance, calculated from the completeness and correctness percentages, was also calculated and ranged from 75% to 80%. Over all areas there are more missed detections than false positives. In some cases, this was due to mixed infrastructure in the same resolution cell of the input SAR data. Drift sand and dune migration covering infrastructure is a concern in many desert regions, and broken segments in the resulting road detections are sometimes due to sand burial. The results also show that, in most cases, the Sentinel–1 vertical transmit–vertical receive (VV) backscatter averages alone constitute the best input to the U–Net model. The detection and monitoring of roads in desert areas are key concerns, particularly given a growing population increasingly on the move.