Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate models

In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object's orientation and on a sensor's flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters

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

Earthquake damage assessment in urban area from Very High Resolution satellite data

The use of remote sensing within the domain of natural hazards and disaster management has become increasingly popular, due in part to increased awareness of environmental issues, including climate change, but also to the improvement of geospatial technologies and the ability to provide high quality imagery to the public through the media and internet. As technology is enhanced, demand and expectations increase for near-real-time monitoring and images to be relayed to emergency services in the event of a natural disaster. During a seismic event, in particular, it is fundamental to obtain a fast and reliable map of the damage of urban areas to manage civil protection interventions. Moreover, the identification of the destruction caused by an earthquake provides seismology and earthquake engineers with informative and valuable data, experiences and lessons in the long term. An accurate survey of damage is also important to assess the economic losses, and to manage and share the resources to be allocated during the reconstruction phase. Satellite remote sensing can provide valuable pieces of information on this regard, thanks to the capability of an instantaneous synoptic view of the scene, especially if the seismic event is located in remote regions, or if the main communication systems are damaged. Many works exist in the literature on this topic, considering both optical data and radar data, which however put in evidence some limitations of the nadir looking view, of the achievable level of details and response time, and the criticality of image radiometric and geometric corrections. The visual interpretation of optical images collected before and after a seismic event is the approach followed in many cases, especially for an operational and rapid release of the damage extension map. Many papers, have evaluated change detection approaches to estimate damage within large areas (e.g., city blocks), trying to quantify not only the extension of the affected area but also the level of damage, for instance correlating the collapse ratio (percentage of collapsed buildings in an area) measured on ground with some change parameters derived from two images, taken before and after the earthquake. Nowadays, remotely sensed images at Very High Resolution (VHR) may in principle enable production of earthquake damage maps at single-building scale. The complexity of the image forming mechanisms within urban settlements, especially of radar images, makes the interpretation and analysis of VHR images still a challenging task. Discrimination of lower grade of damage is particularly difficult using nadir looking sensors. Automatic algorithms to detect the damage are being developed, although as matter of fact, these works focus very often on specific test cases and sort of canonical situations. In order to make the delivered product suitable for the user community, such for example Civil Protection Departments, it is important to assess its reliability on a large area and in different and challenging situations. Moreover, the assessment shall be directly compared to those data the final user adopts when carrying out its operational tasks. This kind of assessment can be hardly found in the literature, especially when the main focus is on the development of sophisticated and advanced algorithms. In this work, the feasibility of earthquake damage products at the scale of individual buildings, which relies on a damage scale recognized as a standard, is investigated. To this aim, damage maps derived from VHR satellite images collected by Synthetic Aperture Radar (SAR) and optical sensors, were systematically compared to ground surveys carried out by different teams and with different purposes and protocols. Moreover, the inclusion of a priori information, such as vulnerability models for buildings and soil geophysical properties, to improve the reliability of the resulting damage products, was considered in this study. The research activity presented in this thesis was carried out in the framework of the APhoRISM (Advanced PRocedures for volcanIc Seismic Monitoring) project, funded by the European Union under the EC-FP7 call. APhoRISM was aimed at demonstrating that an appropriate management and integration of satellite and ground data can provide new improved products useful for seismic and volcanic crisis management

GIS-based urban land use characterization and population modeling with subpixel information measured from remote sensing data

This dissertation provides deeper understanding on the application of Vegetation-Impervious Surface-Soil (V-I-S) model in the urban land use characterization and population modeling, focusing on New Orleans area. Previous research on the V-I-S model used in urban land use classification emphasized on the accuracy improvement while ignoring the discussion of the stability of classifiers. I developed an evaluation framework by using randomization techniques and decision tree method to assess and compare the performance of classifiers and input features. The proposed evaluation framework is applied to demonstrate the superiority of V-I-S fractions and LST for urban land use classification. It could also be applied to the assessment of input features and classifiers for other remote sensing image classification context. An innovative urban land use classification based on the V-I-S model is implemented and tested in this dissertation. Due to the shape of the V-I-S bivariate histogram that resembles topological surfaces, a pattern that honors the Lu-Weng’s urban model, the V-I-S feature space is rasterized into grey-scale image and subsequently partitioned by marker-controlled watershed segmentation, leading to an urban land use classification. This new approach is proven to be insensitive to the selection of initial markers as long as they are positioned around the underlying watershed centers. This dissertation links the population distribution of New Orleans with its physiogeographic conditions indicated by the V-I-S sub-pixel composition and the land use information. It shows that the V-I-S fractions cannot be directly used to model the population distribution. Both the OLS and GWR models produced poor model fit. In contrast, the land use information extracted from the V-I-S information and LST significantly improved regression models. A three-class land use model is fitted adequately. The GWR model reveals the spatial nonstationarity as the relationship between the population distribution and the land use is relatively poor in the city center and becomes stronger towards the city fringe, depicting a classic urban concentric pattern. It highlighted that New Orleans is a complex metropolitan area, and its population distribution cannot be fully modeled with the physiogeographic measurements

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Operationalization of Remote Sensing Solutions for Sustainable Forest Management

The great potential of remote sensing technologies for operational use in sustainable forest management is addressed in this book, which is the reprint of papers published in the Remote Sensing Special Issue “Operationalization of Remote Sensing Solutions for Sustainable Forest Management”. The studies come from three continents and cover multiple remote sensing systems (including terrestrial mobile laser scanning, unmanned aerial vehicles, airborne laser scanning, and satellite data acquisition) and a diversity of data processing algorithms, with a focus on machine learning approaches. The focus of the studies ranges from identification and characterization of individual trees to deriving national- or even continental-level forest attributes and maps. There are studies carefully describing exercises on the case study level, and there are also studies introducing new methodologies for transdisciplinary remote sensing applications. Even though most of the authors look forward to continuing their research, nearly all studies introduced are ready for operational use or have already been implemented in practical forestry