Deep Learning for UAV Imagery Segmentation: The Detection of Elymus Athericus Spread in The Hallig Nordstrandischmoor

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIt has been observed that Athyrics Elymus engulfs smaller species in low marsh habitats in different areas of Europe. To obtain automated segmentation of the Athurics Elymus in Hallig Nordstrandischmoor, a deep learning model based on the transfer learning technique presented in the VGG16 was implemented with a U-Net deep learning architecture and the data augmentation algorithm. In conclusion, the final results were good, and had been grouped into three levels of accuracy. These groups are also characterized by varying levels of diversity in their environments. The wildrye covers the majority of these single images, with a low percentage of other habitats in the first group with an accuracy greater than 96%. In the second group, the accuracy ranged between 91-95%, which included more elements of variation; however, wildrye was still included. Moreover, the third group, with an accuracy rate between 80-90%, did not seem to include wildrye very often, while at the same time including elements not found in the training dataset, such as surface water and dirt roads. Due to this, time and effort are significantly reduced while high accuracy is achieved

Integrating openstreetmap data and sentinel-2 Imagery for classifying and monitoring informal settlements

Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesThe identification and monitoring of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims at integrating OSM data and sentinel-2 imagery for classifying and monitoring the growth of informal settlements methods to map informal areas in Kampala (Uganda) and Dar es Salaam (Tanzania) and to monitor their growth in Kampala. Three building feature characteristics of size, shape and Distance to nearest Neighbour were derived and used to cluster and classify informal areas using Hotspot Cluster analysis and ML approach on OSM buildings data. The resultant informal regions in Kampala were used with Sentinel-2 image tiles to investigate the spatiotemporal changes in informal areas using Convolutional Neural Networks (CNNs). Results from Optimized Hot Spot Analysis and Random Forest Classification show that Informal regions can be mapped based on building outline characteristics. An accuracy of 90.3% was achieved when an optimally trained CNN was executed on a test set of 2019 satellite image tiles. Predictions of informality from new datasets for the years 2016 and 2017 provided promising results on combining different open source geospatial datasets to identify, classify and monitor informal settlements

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

Investigar, publicar y divulgar. Ciencia en infografías

Las infografías aquí reunidas fueron elaboradas después de hacer un monitoreo permanente durante el 2020 de los artículos publicados por los investigadores de la Universidad EAFIT. Este esfuerzo de divulgación se enmarca en la definición del Sistema de Descubrimiento y Creación (2020) de la Universidad EAFIT que busca, entre otras cosas, la movilización de los proyectos de investigación, investigación-creación y creación, conectados con el entorno local, nacional e internacional. Este libro es una de las acciones que ponen en movilización el subsistema de "Difusión y divulgación del conocimiento”, que pretende generar una cultura de la comunicación de la ciencia a públicos diversos, con contenidos pertinentes y rigurosos, inspirados en la excelencia como principal fundamento de la actividad científica.Investigar, publicar y divulgar. Ciencia en infografías recoge más de 50 investigaciones llevadas a cabo por investigadores de la Universidad EAFIT, que fueron publicadas en revistas especializadas y libros resultado de investigación. Cada una de las infografías es producto del trabajo hecho durante seis meses, en los cuales la capacidad de análisis, el tratamiento riguroso de la información, la creatividad y el esfuerzo en los procesos de síntesis fueron primordiales; así como la colaboración entre investigadores y comunicadores para mantener el rigor científico, llegar a acuerdos sobre conceptos que debían convertirse en gráficos y adecuar el uso del lenguaje para diferentes medios y formatos. Por eso, se extiende un agradecimiento especial a todos los científicos que estuvieron disponibles para revisar, corregir y proponer desde su experticia.Universidad EAFI