scholarly journals Building Stock and Building Typology of Kigali, Rwanda

Data ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 105 ◽  
Author(s):  
Felix Bachofer ◽  
Andreas Braun ◽  
Florian Adamietz ◽  
Sally Murray ◽  
Pablo d’Angelo ◽  
...  
Keyword(s):  
Urban Morphology ◽  
Aerial Images ◽  
Municipal Finance ◽  
Building Stock ◽  
Object Based Image Analysis ◽  
Object Based ◽  
The Social ◽  
Building Typology ◽  
The City ◽  
Very High

This study uses very high-resolution Pléiades imagery for the densely built-up central part of the City of Kigali for the year 2015 in order to derive urban morphology data on building footprints, building archetypes and building heights. Aerial images of the study area from 2008–2009 were used in combination with the 2015 dataset to create a change monitoring dataset on a single building basis. A semi-automated approach was chosen which combined an object-based image analysis with an expert-based revision. The result is a geospatial dataset that detects 165,625 buildings for 2008–2009 and 211,458 for 2015. The dataset includes information on the type of changes between the two dates. Analysis of this geospatial dataset can be used for a range of research applications in economics and the social sciences, as well as a range of policy applications in urban planning and municipal finance administration.

scholarly journals Mask R-CNN and OBIA Fusion Improves the Segmentation of Scattered Vegetation in Very High-Resolution Optical Sensors

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 320
Author(s):  
Emilio Guirado ◽  
Javier Blanco-Sacristán ◽  
Emilio Rodríguez-Caballero ◽  
Siham Tabik ◽  
Domingo Alcaraz-Segura ◽  
...  
Keyword(s):  
High Resolution ◽  
Optical Sensors ◽  
Biotic Interactions ◽  
Relevant Information ◽  
Vegetation Monitoring ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Segmentation Methods ◽  
High Resolution Images ◽  
Very High

Vegetation generally appears scattered in drylands. Its structure, composition and spatial patterns are key controls of biotic interactions, water, and nutrient cycles. Applying segmentation methods to very high-resolution images for monitoring changes in vegetation cover can provide relevant information for dryland conservation ecology. For this reason, improving segmentation methods and understanding the effect of spatial resolution on segmentation results is key to improve dryland vegetation monitoring. We explored and analyzed the accuracy of Object-Based Image Analysis (OBIA) and Mask Region-based Convolutional Neural Networks (Mask R-CNN) and the fusion of both methods in the segmentation of scattered vegetation in a dryland ecosystem. As a case study, we mapped Ziziphus lotus, the dominant shrub of a habitat of conservation priority in one of the driest areas of Europe. Our results show for the first time that the fusion of the results from OBIA and Mask R-CNN increases the accuracy of the segmentation of scattered shrubs up to 25% compared to both methods separately. Hence, by fusing OBIA and Mask R-CNNs on very high-resolution images, the improved segmentation accuracy of vegetation mapping would lead to more precise and sensitive monitoring of changes in biodiversity and ecosystem services in drylands.

scholarly journals Building a timeline, developing a narrative: visualising fringe belt formation alongside street network development

2017 ◽  
Author(s):  
Ilaria Geddes ◽  
Nadia Charalambous
Keyword(s):  
Urban Space ◽  
Urban Morphology ◽  
Composite Methods ◽  
The Social ◽  
Cyclical Process ◽  
Diachronic Analysis ◽  
Generative Mechanisms ◽  
The Relationship ◽  
The City ◽  
Related Paper

This project was developed as an attempt to assess the relationship between different morphogenetic processes, in particular, those of fringe belt formation as described by M.R.G. Conzen (1960) and Whitehand (2001), and of centrality and compactness as described by Hillier (1999; 2002). Different approaches’ focus on different elements of the city has made it difficult to establish exactly how these processes interact or whether they are simply different facets of development reflecting wider socio-economic factors. To address this issue, a visual, chronological timeline of Limassol’s development was constructed along with a narrative of the socio-economic context of its development.  The complexity of cities, however, makes static visualisations across time difficult to read and assess alongside textual narratives. We therefore took the step of developing an animation of land use and configurational analyses of Limassol, in order bring to life the diachronic analysis of the city and shed light on its generative mechanisms. The video presented here shows that the relationship between the processes mentioned above is much stronger and more complex than previously thought. The related paper explores in more detail the links between fringe belt formation as a cyclical process of peripheral development and centrality as a recurring process of minimisation of gains in distance. The project’s outcomes clearly show that composite methods of visualisations are an analytical opportunity still little exploited within urban morphology. References Conzen, M.R.G., 1960. Alnwick, Northumberland: A Study in Town-Plan Analysis, London: Institute of British Geographers. Hillier, B., 2002. A Theory of the City as Object: or how spatial laws mediate the social construction of urban space. Urban Des Int, 7(3–4), pp.153–179. Hillier, B., 1999. Centrality as a process: accounting for attraction inequalities in deformed grids. Urban Des Int, 4(3–4), pp.107–127. Whitehand, J.W.R., 2001. British urban morphology: the Conzenian tradition. Urban Morphology, 5(2), pp.103–109.

scholarly journals Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach

2018 ◽  
Author(s):  
Ahmad Fallatah ◽  
Simon Jones ◽  
David Mitchell
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Hybrid Approach ◽  
Informal Settlements ◽  
Validation Dataset ◽  
Relative Importance ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Machine Learning Approach ◽  
Very High

The identification 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 to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

Identification of Soil Erosion in Alpine Grasslands on High-Resolution Aerial Images: Switching from Object-based Image Analysis to Deep Learning?

2020 ◽  
Author(s):  
Lauren Zweifel ◽  
Maxim Samarin ◽  
Katrin Meusburger ◽  
Volker Roth ◽  
Christine Alewell
Keyword(s):  
Land Use ◽  
Image Analysis ◽  
Soil Erosion ◽  
Soil Degradation ◽  
Aerial Images ◽  
Alpine Grasslands ◽  
Object Based Image Analysis ◽  
Erosion Processes ◽  
Object Based ◽  
Land Use Practices

<p>Soil erosion in Alpine grassland areas is an ecological threat caused by the extreme topography, prevailing climate conditions and land-use practices but enhanced by climate change (e.g., heavy precipitation events, changing snow dynamics) in combination with changing land-use practices (e.g, more intensely used pastures). To increase our understanding of ongoing soil erosion processes in Alpine grasslands, there is a need to acquire detailed information on spatial extension and temporal trends.</p><p>In the past, we have successfully applied a semi-automatic method using an object-based image analysis (OBIA) framework with high-resolution aerial images (0.25-0.5m) and a digital terrain model (2m) to map erosion features in the Central Swiss Alps (Urseren Valley, Canton Uri, Switzerland). Degraded sites are classified according to the major erosion process (shallow landslides; sites with reduced vegetation cover affected by sheet erosion) or triggering factors (trampling by livestock; management effects) (Zweifel et al. 2019). We now aim to apply a deep learning (DL) model with the purpose of fast and efficient spatial upscaling(e.g., alpine-wide analysis). While OBIA yields high quality results, there are multiple constraints, such as labor-intensive steps and the requirement of expert knowledge, which make the method unsuitable for larger scale applications. The results of OBIA are used as a training dataset for our DL model. The DL approach uses fully-convolutional networks with the U-Net architecture and is capable of rapid segmentation and classification to identify areas with reduced vegetation cover and bare soil sites.</p><p>Results for the Urseren Valley (Canton Uri, Switzerland) show an increase in total area affected by soil degradation of 156 ±18% during a 16-year observation period (2000-2016). A comparison of the two methods (OBIA and DL) shows that DL results for the Urseren Valley follow similar trends for the 16-year period and that the segmentations of eroded sites are in good agreement (IoU = 0.83). First transferability tests to other valleys not considered during training of the DL model are very promising, confirming that DL is a well-suited and efficient method for future projects to map and assess soil erosion processes in grassland areas at regional scales.</p><p> </p><p><strong>References</strong></p><p>L. Zweifel, K. Meusburger, and C. Alewell. Spatio-temporal pattern of soil degradation in a Swiss Alpine grassland catchment. Remote Sensing of Environment, 235, 2019.</p>

scholarly journals Spatiotemporal Changes in 3D Building Density with LiDAR and GEOBIA: A City-Level Analysis

2020 ◽  
Vol 12 (21) ◽  
pp. 3668
Author(s):  
Karolina Zięba-Kulawik ◽  
Konrad Skoczylas ◽  
Ahmed Mustafa ◽  
Piotr Wężyk ◽  
Philippe Gerber ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Urban Expansion ◽  
3D Models ◽  
Object Based Image Analysis ◽  
Airborne Laser ◽  
Object Based ◽  
Geographic Object ◽  
The City ◽  
New Buildings ◽  
Level Analysis

Understanding how, where, and when a city is expanding can inform better ways to make our cities more resilient, sustainable, and equitable. This paper explores urban volumetry using the Building 3D Density Index (B3DI) in 2001, 2010, 2019, and quantifies changes in the volume of buildings and urban expansion in Luxembourg City over the last two decades. For this purpose, we use airborne laser scanning (ALS) point cloud (2019) and geographic object-based image analysis (GEOBIA) of aerial orthophotos (2001, 2010) to extract 3D models, footprints of buildings and calculate the volume of individual buildings and B3DI in the frame of a 100 × 100 m grid, at the level of parcels, districts, and city scale. Findings indicate that the B3DI has notably increased in the past 20 years from 0.77 m3/m2 (2001) to 0.9 m3/m2 (2010) to 1.09 m3/m2 (2019). Further, the increase in the volume of buildings between 2001–2019 was +16 million m3. The general trend of changes in the cubic capacity of buildings per resident shows a decrease from 522 m3/resident in 2001, to 460 m3/resident in 2019, which, with the simultaneous appearance of new buildings and fast population growth, represents the dynamic development of the city.

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