Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe need of timely and accurate information for the territory has increased over the years, making Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing. Recently, the advances in satellite technology and the open access policies for remote sensing data increased the interest in exploring satellite image time series. In addition, the attention of researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential information. However, acquiring reference data to train these algorithms is still a hurdle. This study aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to increase the performance of our models, testing three different oversampling techniques. The results attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is possible to produce LCLU classification of satellite image time series using only open-source data by using Sentinel-2 imagery and the LUCAS survey as refence data

Mapping Europe into local climate zones

Cities are major drivers of environmental change at all scales and are especially at risk from the ensuing effects, which include poor air quality, flooding and heat waves. Typically, these issues are studied on a city-by-city basis owing to the spatial complexity of built landscapes, local topography and emission patterns. However, to ensure knowledge sharing and to integrate local-scale processes with regional and global scale modelling initiatives, there is a pressing need for a world-wide database on cities that is suited for environmental studies. In this paper we present a European database that has a particular focus on characterising urbanised landscapes. It has been derived using tools and techniques developed as part of the World Urban Database and Access Portal Tools (WUDAPT) project, which has the goal of acquiring and disseminating climate-relevant information on cities worldwide. The European map is the first major step toward creating a global database on cities that can be integrated with existing topographic and natural land-cover databases to support modelling initiatives

Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)

Understanding the use of current land cover, along with monitoring change over time, is vital for agronomists and agricultural agencies responsible for land management. The increasing spatial and temporal resolution of globally available satellite images, such as provided by Sentinel-2, creates new possibilities for researchers to use freely available multi-spectral optical images, with decametric spatial resolution and more frequent revisits for remote sensing applications such as land cover and crop classification (LC&CC), agricultural monitoring and management, environment monitoring. Existing solutions dedicated to cropland mapping can be categorized based on per-pixel based and object-based. However, it is still challenging when more classes of agricultural crops are considered at a massive scale. In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types. The proposed methodology is capable of automated feature extraction by learning time correlation of multiple images, which reduces manual feature engineering and modeling crop phenological stages. Fifteen classes, including major agricultural crops, were considered in this study. We also tested other widely used traditional machine learning algorithms for comparison such as support vector machine SVM, random forest (RF), Kernal SVM, and gradient boosting machine, also called XGBoost. The overall accuracy achieved by our proposed Pixel R-CNN was 96.5%, which showed considerable improvements in comparison with existing mainstream methods. This study showed that Pixel R-CNN based model offers a highly accurate way to assess and employ time-series data for multi-temporal classification tasks

GLOBAL BARE GROUND GAIN BETWEEN 2000 AND 2012 AND THE RELATIONSHIP WITH SOCIOECONOMIC DEVELOPMENT

Bare ground gain -- the complete removal of vegetation due to land use changes, represents an extreme land cover transition that completely alters the structure and functioning of ecosystems. The fast expansion of bare ground cover is directly associated with increasing population and urbanization, resulting in accelerated greenhouse gas emissions, intensified urban heat island phenomenon, and extensive habitat fragments and loss. While the economic return of settlement and infrastructure construction has improved human livelihoods, the negative impacts on the environment have disproportionally affected vulnerable population, creating inequality and tension in society. The area, distribution, drivers, and change rates of global bare ground gain were not systematically quantified; neither was the relationship between such dynamics and socioeconomic development. This dissertation seeks methods for operational characterization of bare ground expansion, advances our understanding of the magnitudes, dynamics, and drivers of global bare ground gain between 2000 and 2012, and uncovers the implications of such change for macro-economic development monitoring, all through Landsat satellite observations. The approach that employs wall-to-wall maps of bare ground gain classified from Landsat imagery for probability sample selection is proved particularly effective for unbiased area estimation of global, continental, and national bare ground gain, as a small land cover and land use change theme. Anthropogenic land uses accounted for 95% of the global bare ground gain, largely consisting of commercial/residential built-up, infrastructure development, and resource extraction. China and the United States topped the total area increase in bare ground. Annual change rates of anthropogenic bare ground gain are found as a leading indicator of macro-economic change in the study period dominated by the 2007-2008 global financial crisis, through econometric analysis between annual gains in the bare ground of different land use outcomes and economic fluctuations in business cycles measured by detrended economic variables. Instead of intensive manual interpretation of land-use attributes of probability sample, an approach of integrating a pixel- and an object- based deep learning algorithms is proposed and tested feasible for automatic attribution of airports, a transportation land use with economic importance

Advancing large-scale analysis of human settlements and their dynamics

Due to the importance for a range of sustainability challenges, it is important to understand the spatial dynamics of human settlements. The rapid expansion of built-up land is among the most extensive global land changes, even though built-up land occupies only a small fraction of the terrestrial biosphere. Moreover, the different ways in which human settlements are manifested are crucially important for their environmental and socioeconomic impacts. Yet, current analysis of human settlements heavily relies on land cover datasets, which typically have only one class to represent human settlements. Consequently, the analysis of human settlements does often not account for the heterogeneity within urban environment or their subtle changes. This simplistic representation severely limits our understanding of change processes in human settlements, as well as our capacity to assess socioeconomic and environmental impacts. This thesis aims to advance large-scale analysis of human settlements and their dynamics through the lens of land systems, with a specific focus on the role of land-use intensity. Chapter 2 explores the use of human settlement systems as an approach to understanding their variation in space and changes over time. Results show that settlement systems exist along a density gradient, and their change trajectories are typically gradual and incremental. In addition, results indicate that the total increase in built-up land in village landscapes outweighs that of dense urban regions. This chapter suggests that we should characterize human settlements more comprehensively to advance the analysis of human settlements, going beyond the emergence of new built-up land in a few mega-cities only. In Chapter 3, urban land-use intensity is operationalized by the horizontal and vertical spatial patterns of buildings. Particularly, I trained three random forest models to estimate building footprint, height, and volume, respectively, at a 1-km resolution for Europe, the US, and China. The models yield R2 values of 0.90, 0.81, and 0.88 for building footprint, height, and volume, respectively. The correlation between building footprint and building height at a pixel level was 0.66, illustrating the relevance of mapping these properties independently. Chapter 4 builds on the methodological approach presented in chapter 3. Specifically, it presents an improved approach to mapping 3D built-up patterns (i.e., 3D building structure), and applies this to map building footprint, height, and volume at a global scale. The methodological improvement includes an optimized model structure, additional explanatory variables, and updated input data. I find distance decay functions from the centre of the city to its outskirts for all three properties for major cities in all continents. Yet, again, the height, footprint (density), and volume differ drastically across these cities. Chapter 5 uses built-up land per person as an operationalization for urban land-use intensity, in order to investigate its temporal dynamics at a global scale. Results suggest that the decrease of urban land-use intensity relates to 38.3%, 49.6%, and 37.5% of the built-up land expansion in the three periods during 1975-2015, but with large local variations. In the Global South, densification often happens in regions where human settlements are already used intensively, suggesting potential trade-offs with other living standards. These chapters represent the recent advancements in large-scale analysis of human settlements by revealing a large variation in urban fabric. Urban densification is widely acknowledged as one of the tangible solutions to satisfy the increased land demand for human settlement while conserving other land, suggesting the relevance of these findings to inform sustainable development. Nevertheless, local settlement trajectories towards intensive forms should also be guided in a large-scale context with broad considerations, including the quality of life for inhabitants, because these trade-offs and synergies remain largely unexplored in this analysis

Google Earth Engine cloud computing platform for remote sensing big data applications: a comprehensive review

Remote sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis. In particular, this platformfacilitates processing big geo data over large areas and monitoring the environment for long periods of time. Although this platformwas launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. For this purpose, 450 journal articles published in 150 journals between January 2010 andMay 2020 were studied. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. Moreover, supervised machine learning algorithms, such as Random Forest, were more widely applied to image classification tasks. GEE has also been employed in a broad range of applications, such as Land Cover/land Use classification, hydrology, urban planning, natural disaster, climate analyses, and image processing. It was generally observed that the number of GEE publications have significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges.Peer ReviewedPostprint (published version

Application of Convolutional Neural Network in the Segmentation and Classification of High-Resolution Remote Sensing Images

Numerous convolution neural networks increase accuracy of classification for remote sensing scene images at the expense of the models space and time sophistication This causes the model to run slowly and prevents the realization of a trade-off among model accuracy and running time The loss of deep characteristics as the network gets deeper makes it impossible to retrieve the key aspects with a sample double branching structure which is bad for classifying remote sensing scene photo