| 1. |
- Ban, Yifang, et al.
(författare)
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Near Real-Time Wildfire Progression Monitoring with Sentinel-1 SAR Time Series and Deep Learning
- 2020
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 10:1
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Tidskriftsartikel (refereegranskat)abstract
- In recent years, the world witnessed many devastating wildfires that resulted in destructive human and environmental impacts across the globe. Emergency response and rapid response for mitigation calls for effective approaches for near real-time wildfire monitoring. Capable of penetrating clouds and smoke, and imaging day and night, Synthetic Aperture Radar (SAR) can play a critical role in wildfire monitoring. In this communication, we investigated and demonstrated the potential of Sentinel-1 SAR time series with a deep learning framework for near real-time wildfire progression monitoring. The deep learning framework, based on a Convolutional Neural Network (CNN), is developed to detect burnt areas automatically using every new SAR image acquired during the wildfires and by exploiting all available pre-fire SAR time series to characterize the temporal backscatter variations. The results show that Sentinel-1 SAR backscatter can detect wildfires and capture their temporal progression as demonstrated for three large and impactful wildfires: the 2017 Elephant Hill Fire in British Columbia, Canada, the 2018 Camp Fire in California, USA, and the 2019 Chuckegg Creek Fire in northern Alberta, Canada. Compared to the traditional log-ratio operator, CNN-based deep learning framework can better distinguish burnt areas with higher accuracy. These findings demonstrate that spaceborne SAR time series with deep learning can play a significant role for near real-time wildfire monitoring when the data becomes available at daily and hourly intervals with the launches of RADARSAT Constellation Missions in 2019, and SAR CubeSat constellations.
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| 2. |
- Belloni, Valeria, et al.
(författare)
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Cosmo-skymed range measurements for displacement monitoring using amplitude persistent scatterers
- 2020
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Ingår i: IGARSS 2020 - 2020 IEEE international geoscience and remote sensing symposium. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2495-2498
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Konferensbidrag (refereegranskat)abstract
- Synthetic Aperture Radar (SAR) satellite data are widely used to monitor deformation phenomena impacting the Earth's surface (e.g. landslides, glacier motions, subsidence, and volcano deformations) and infrastructures (e.g. bridges, dams, buildings). The analysis is generally performed using the Differential SAR Interferometry (DInSAR) technique that exploits the phase information of SAR data. However, this technique suffers for lack of coherence among the considered stack of images, and it can only be adopted to monitor slow deformation phenomena. In the field of geohazards monitoring and glacier melting, the Offset Tracking technique has been also widely investigated. This approach is based on the amplitude information only but it reaches worse accuracy compared to DInSAR. To overcome the limitations of DInSAR and Offset Tracking, in the last decade, a new technique called Imaging Geodesy has been investigated exploiting the amplitude information and the precise orbit of the modern SAR platforms (i.e. TerraSAR-X, COSMO-SkyMed). In this study, an investigation of using COSMO-SkyMed data for Earth surface monitoring was performed. The developed approach was applied to a set of imagery acquired over the Corvara (Northern Italy) area, which is affected by a fast landslide with yearly displacements up to meters. Specifically, two well identifiable and stable human-made Amplitude Persistent Scatterers (APSs) were considered to estimate the residual errors of COSMO-SkyMed sensor during the acquisition period between 2010 and 2015. Then, the same methodology was applied to estimate the displacement of a Corner Reflector (CR) located in the landslide area. Finally, the results were compared to the available GPS reference trend showing a good agreement.
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| 3. |
- Belloni, Valeria, et al.
(författare)
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Crack Monitoring from Motion (CMfM) : Crack detection and measurement using cameras with non-fixed positions
- 2023
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Ingår i: Automation in Construction. - : Elsevier BV. - 0926-5805 .- 1872-7891. ; 156
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Tidskriftsartikel (refereegranskat)abstract
- The assessment of cracks in civil infrastructures commonly relies on visual inspections carried out at night, resulting in limited inspection time and an increased risk of crack oversight. The Digital Image Correlation (DIC) technique, employed in structural monitoring, requires stationary cameras for image collection, which proves challenging for long-term monitoring. This paper describes the Crack Monitoring from Motion (CMfM) methodology for automatically detecting and measuring cracks using non-fixed cameras, combining Convolutional Neural Networks and photogrammetry. Through evaluation using images obtained from laboratory tests on concrete beams and subsequent comparison with DIC and a pointwise sensor, CMfM demonstrates accurate crack width computation within a few hundredths of a millimetre when compared to the sensor. This method exhibits potential for effectively monitoring temporal crack evolution using non-fixed cameras.
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| 4. |
- Belloni, V., et al.
(författare)
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Digital image correlation from commercial to FOS software : A mature technique for full-field displacement measurements
- 2018
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Ingår i: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives. - : International Society for Photogrammetry and Remote Sensing. ; , s. 91-95
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Konferensbidrag (refereegranskat)abstract
- In the last few decades, there has been a growing interest in studying non-contact methods for full-field displacement and strain measurement. Among such techniques, Digital Image Correlation (DIC) has received particular attention, thanks to its ability to provide these information by comparing digital images of a sample surface before and after deformation. The method is now commonly adopted in the field of civil, mechanical and aerospace engineering and different companies and some research groups implemented 2D and 3D DIC software. In this work a review on DIC software status is given at first. Moreover, a free and open source 2D DIC software is presented, named py2DIC and developed in Python at the Geodesy and Geomatics Division of DICEA of the University of Rome "La Sapienza"; its potentialities were evaluated by processing the images captured during tensile tests performed in the Structural Engineering Lab of the University of Rome "La Sapienza" and comparing them to those obtained using the commercial software Vic-2D developed by Correlated Solutions Inc, USA. The agreement of these results at one hundredth of millimetre level demonstrate the possibility to use this open source software as a valuable 2D DIC tool to measure full-field displacements on the investigated sample surface.
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| 5. |
- Belloni, Valeria, et al.
(författare)
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py2DIC : A New Free and Open Source Software for Displacement and Strain Measurements in the Field of Experimental Mechanics
- 2019
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Ingår i: Sensors. - : MDPI. - 1424-8220. ; 19:18
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Tidskriftsartikel (refereegranskat)abstract
- Thanks to the advances in computer power, memory storage and the availability of low-cost and high resolution digital cameras, Digital Image Correlation (DIC) is currently one of the most used optical and non-contact techniques for measuring material deformations. A free and open source 2D DIC software, named py2DIC, was developed at the Geodesy and Geomatics Division of the Sapienza University of Rome. Implemented in Python, the software is based on the template matching method and computes the 2D displacements and strains of samples subjected to mechanical loading. In this work, the potentialities of py2DIC were evaluated by processing two different sets of experimental data and comparing the results with other three well known DIC software packages Ncorr, Vic-2D and DICe. Moreover, an accuracy assessment was performed comparing the results with the values independently measured by a strain gauge fixed on one of the samples. The results demonstrate the possibility of successfully characterizing the deformation mechanism of the investigated materials, highlighting the pros and cons of each software package.
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| 6. |
- Belloni, Valeria, et al.
(författare)
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Tack Project: Tunnel and bridge automatic crack monitoring using deep learning and photogrammetry
- 2020
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Ingår i: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. - : Copernicus GmbH. ; , s. 741-745
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Konferensbidrag (refereegranskat)abstract
- Civil infrastructures, such as tunnels and bridges, are directly related to the overall economic and demographic growth of countries. The aging of these infrastructures increases the probability of catastrophic failures that results in loss of lives and high repair costs; all over the world, these factors drive the need for advanced infrastructure monitoring systems. For these reasons, in the last years, different types of devices and innovative infrastructure monitoring techniques have been investigated to automate the process and overcome the main limitation of standard visual inspections that are used nowadays. This paper presents some preliminary findings of an ongoing research project, named TACK, that combines advanced deep learning techniques and innovative photogrammetric algorithms to develop a monitoring system. Specifically, the project focuses on the development of an automatic procedure for crack detection and measurement using images of tunnels and bridges acquired with a mobile mapping system. In this paper, some preliminary results are shown to investigate the potential of a deep learning algorithm in detecting cracks occurred in concrete material. The model is a CNN (Convolutional Neural Network) based on the U-Net architecture; in this study, we tested the transferability of the model that has been trained on a small available labeled dataset and tested on a large set of images acquired using a customized mobile mapping system. The results have shown that it is possible to effectively detect cracks in unseen imagery and that the primary source of errors is the false positive detection of crack-like objects (i.e., contact wires, cables and tile borders).
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| 7. |
- Furberg, Dorothy, et al.
(författare)
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Monitoring of Urbanization and Analysis of Environmental Impact in Stockholm with Sentinel-2A and SPOT-5 Multispectral Data
- 2019
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 11:20
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Tidskriftsartikel (refereegranskat)abstract
- There has been substantial urban growth in Stockholm, Sweden, the fastest-growingcapital in Europe. The intensifying urbanization poses challenges for environmental managementand sustainable development. Using Sentinel-2 and SPOT-5 imagery, this research investigatesthe evolution of land-cover change in Stockholm County between 2005 and 2015, and evaluatesurban growth impact on protected green areas, green infrastructure and urban ecosystem serviceprovision. One scene of 2015 Sentinel-2A multispectral instrument (MSI) and 10 scenes of 2005SPOT-5 high-resolution instruments (HRI) imagery over Stockholm County are classified into 10land-cover categories using object-based image analysis and a support vector machine algorithmwith spectral, textural and geometric features. Reaching accuracies of approximately 90%, theclassifications are then analyzed to determine impact of urban growth in Stockholm between 2005and 2015, including land-cover change statistics, landscape-level urban ecosystem service provisionbundle changes and evaluation of regional and local impact on legislatively protected areas as well asecologically significant green infrastructure networks. The results indicate that urban areas increasedby 15%, while non-urban land cover decreased by 4%. In terms of ecosystem services, changes inproximity of forest and low-density built-up areas were the main cause of lowered provision oftemperature regulation, air purification and noise reduction. There was a decadal ecosystem serviceloss of 4.6 million USD (2015 exchange rate). Urban areas within a 200 m buer zone around theSwedish environmental protection agency’s nature reserves increased 16%, with examples of urbanareas constructed along nature reserve boundaries. Urban expansion overlapped the deciduousecological corridor network and green wedge/core areas to a small but increasing degree, often inclose proximity to weak but important green links in the landscape. Given these findings, increasedconservation/restoration focus on the region’s green weak links is recommended.
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| 8. |
- Hafner, Sebastian, et al.
(författare)
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Exploring The Fusion Of Sentinel-1 Sar And Sentinel-2 Msi Data For Built-Up Area Mapping Using Deep Learning
- 2021
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS). - : Institute of Electrical and Electronics Engineers Inc.. ; , s. 4720-4723
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Konferensbidrag (refereegranskat)abstract
- This research explores the potential of combining Sentinel-1 C-band Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data for Built-Up Area (BUA) mapping using deep learning. A lightweight U-Net model is trained using openly available building footprint reference data in North America and tested in four cities across three additional continents. The best test performance in terms of F1 score was achieved by the joint use of SAR and multi-spectral data (0.676), followed by multi-spectral (0.611) and SAR data (0.601). The developed fusion approach is particularly promising to distinguish BUA in low-density residential neighborhoods. Furthermore, our fusion approach compares favorably to the state-of-the-art in BUA mapping in the selected cities. However, associated with the diverse characteristics of human settlements around the world, considerable differences in accuracy among the test cities were observed. This indicates the need for more sophisticated fusion techniques to improve CNN model generalization and for adding more diverse training data.
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| 9. |
- Hafner, Sebastian, et al.
(författare)
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Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping
- 2023
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Ingår i: 2023 Joint Urban Remote Sensing Event, JURSE 2023. - : Institute of Electrical and Electronics Engineers (IEEE).
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Konferensbidrag (refereegranskat)abstract
- Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aperture Radar (SAR) and optical data. However, multi-modal networks may rely on just one modality due to the greedy nature of learning. In turn, the imbalanced utilization of modalities can negatively affect the generalization ability of a network. In this paper, we investigate the utilization of SAR and optical data for urban mapping. To that end, a dual-branch network architecture using intermediate fusion modules to share information between the uni-modal branches is utilized. A cutoff mechanism in the fusion modules enables the stopping of information flow between the branches, which is used to estimate the network's dependence on SAR and optical data. While our experiments on the SEN12 Global Urban Mapping dataset show that good performance can be achieved with conventional SAR-optical data fusion (F1 score = 0.682±0.014), we also observed a clear under-utilization of optical data. Therefore, future work is required to investigate whether a more balanced utilization of SAR and optical data can lead to performance improvements.
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| 10. |
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