Deep learning in remote sensing: a review

Standing at the paradigm shift towards data-intensive science, machine learning techniques are becoming increasingly important. In particular, as a major breakthrough in the field, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we resist a 'black-box' solution? There are controversial opinions in the remote sensing community. In this article, we analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with. More importantly, we advocate remote sensing scientists to bring their expertise into deep learning, and use it as an implicit general model to tackle unprecedented large-scale influential challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

Multisource data integration to investigate one century of evolution for the Agnone landslide (Molise, southern Italy)

Landslides are one of the most relevant geohazards worldwide, causing direct and indirect costs and fatalities. Italy is one of the countries most affected by mass movements, and the Molise region, southern Italy, is known to be susceptible to erosional processes and landslides. In January 2003, a landslide in the municipality of Agnone, in the Colle Lapponi-Piano Ovetta (CL-PO) territory, occurred causing substantial damage to both structures and civil infrastructure. To investigate the evolution of the landslide-affected catchment over approximately one century, different data were taken into account: (i) literature information at the beginning of the twentieth century; (ii) historical sets of aerial optical photographs to analyse the geomorphological evolution from 1945 to 2003; (iii) SAR (Synthetic Aperture Radar) data fromthe ERS1/2, ENVISATand COSMO-SkyMed satellites tomonitor the displacement from 1992 to 2015; (iv) traditional measurements carried out through geological and geomorphological surveys, inclinometers and GPS campaigns to characterize the geological setting of the area; and (v) recent optical photographs of the catchment area to determine the enlargement of the landslide. Using the structure from motion technique, a 3D reconstruction of each set of historical aerial photographs was made to investigate the geomorphological evolution and to trace the boundary of the mass movements. As a result, the combination of multitemporal and multitechnique analysis of the evolution of the CL-PO landslide enabled an assessment of the landslide expansion, which resulted in a maximum length of up to approximately 1500 m. A complete investigation of the past and present deformational sequences of the area was performed to potentially plan further mitigation and prevention strategies to avoid possible reactivations

Cell-Based Deformation Monitoring via 3D Point Clouds

Deformation is one of the most important phenomena in environmental science and engineering. Deformation of artificial and natural objects happens worldwide, such as structural deformation, landslide, subsidence, erosion, and rockfall. Monitoring and assessment of such deformation process is not only scientifically interesting, but also beneficial to hazard/risk control and prediction. In addition, it is also useful for regional planning and development. Deformation monitoring was driven by geodetic observations in the field of traditional geodetic surveying, based on the measurement of sparse points in a control network. Recently, with the rapid development of terrestrial LiDAR techniques, millions of points with associated three-dimensional coordinates (known as "3D point clouds") can be promptly captured in a few minutes. Compared to traditional surveying, terrestrial LiDAR offers great potential for deformation monitoring, because of various advantages such as fast data capture, high data density, and precise 3D object representation. By analysing 3D point clouds, the objective of this thesis is to provide an effective and efficient approach for deformation monitoring. Towards this goal, this thesis designs a new concept of "deformation map" for deformation representation and a novel "cell-based approach" for deformation computation. The main outcome of this thesis is a novel and rich approach that is able to automatically and incrementally compute a deformation map that enables a better understanding of structural and natural hazards with heterogeneous deformation characteristics. This work includes several dedicated contributions as follows. Hybrid Deformation Modelling. This thesis firstly provides a comprehensive investigation on the modelling requirements of various deformation phenomena. The requirements concern three main aspects, i.e., what has deformation (deformation object), which type of deformation, and how to describe deformation. Based on this detailed requirement analysis, we propose a rich and hybrid deformation model. This model is composed of meta-deformation, sub-deformation and deformation map, corresponding to deformation for a small cell, for a partial area, and for the whole object, respectively. Cell-based Deformation Computation. In order to automatically and incrementally extract heterogeneous deformation of the whole monitored object, we bring the "cell" concept into deformation monitoring. This thesis builds a cell-based deformation computing framework, which consists of three key steps: split, detect, and merge. Split is to divide the space of the object into many cells (uniform or irregular); detect is to extract the meta-deformation for individual cells by analysing the inside point clouds at two epochs; and merge is to group adjacent cells with similar deformation together and to form a consistent sub-deformation. As the final result, an informative deformation map is computed for describing the deformation for the whole object. Evaluation of Cell-based Approach. To evaluate such hybrid modelling and cell-based deformation computation, this thesis extensively studies both synthetic and real-life point cloud datasets: (1) by imitating a landslide scenario, we generate synthetic data using Matlab programming and practical settings, and compare the cell-based approach with traditional non-cell based geodetic methods; (2) by analysing two real-life cases of deformation in Switzerland, we further validate our approach and compare the results with third party sources (e.g., results provided by a surveying company, results computed by using a commercial software like 3DReshaper). Extension of Cell-based Approach. At the last stages of this thesis work, we particularly focus on providing several technical extensions to enhance this cell-based deformation monitoring approach. The main extensions include: (1) supporting dynamic cells instead of uniform cells when splitting the entire object space, (2) finding cell correspondence for the deformation scenarios that have large deformation like rockfalls, (3) movement tracking with data-driven cells which have irregular cell shape that can be automatically determined by the deformation boundary itself, (4) designing an adaptive modelling strategy that is able to accordingly select a suitable model for detecting meta-deformation of cells, and (5) computing deformation evolution for a monitored object with more than two epochs of point cloud datasets