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Thesis_draft0.2.bbl
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\field{abstract}{Building maps have a plethora of applications in government, industry and academia. In most cases, large scale maps can be retrieved from {OpenStreetMap} vector data. However, for certain rapidly changing built and semi-built environments, corresponding maps are not as accurate and contain label noise such as missing, incorrectly present, shifted labels, etc.; mainly because buildings in those regions are constantly being constructed, deconstructed, replaced and altered. One such case is extant in the Rohingya camps of southeastern border region of Bangladesh. Mass refugee influx in late 2017 and following population growth has necessitated the construction of buildings and expansion of camps. Consequently, reliable methods are necessary for detecting and documenting camp buildings. Ultra-high-resolution drone images of Rohingya camps are semantically segmented through fully convolutional U-Net deep learning systems for generating accurate building maps from noisy labels. A wide variety of noises are prevalent in the labels. Deep learning systems provide less noisy predictions compared to the classification tool in the most widely used Geographic Information System ({GIS}) software, {ArcGIS}. Data augmentation and regularization allows reliable learning, even in the presence of label noise. During testing, calculation of numeric performance metrics against noisy labels can grossly underestimate true skill and performance of the model. A subset of 22 million pixels of the testing data is relabelled by hand to obtain noise-free labels. Testing our generated maps against noisy and noise-free labels confirms that true performance is higher than otherwise indicated by freely available building maps. Empirical results reveal that utilized pipeline is able to learn from noisy data and produce labels which are more accurate and less noisy. Labels generated by our best performing system provide Intersection-over-Union ({IoU}) gain of 17.6\% and Dice score gain of 13.6\% over freely available labels from {OpenStreetMap}. Finally, spatio-temporal building maps are generated to portray the applicability of this research.}
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\field{issn}{0143-1161}
\field{journaltitle}{International Journal of Remote Sensing}
\field{month}{11}
\field{note}{Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/01431161.2020.1763496}
\field{number}{21}
\field{title}{Learning to extract buildings from ultra-high-resolution drone images and noisy labels}
\field{urlday}{31}
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\field{urlyear}{2021}
\field{volume}{41}
\field{year}{2020}
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\field{abstract}{We combine nighttime lights data, official statistics, and new household survey data from northern Kenya in order to assess the impact of long-term refugee camps on host populations. The nighttime lights estimates show that refugee inflows increase economic activity in areas very close to Kakuma refugee camp: the elasticity of the luminosity index to refugee population is 0.36 within a 10 km distance from the camp center. In addition, household consumption within the same proximity to the camp is 25\% higher than in areas farther away. Price, household survey, and official statistics suggest that the mechanisms driving this positive effect are increased availability of new employment and price changes in agricultural and livestock markets that are favorable to local producers.}
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\field{issn}{0304-3878}
\field{journaltitle}{Journal of Development Economics}
\field{langid}{english}
\field{month}{1}
\field{shortjournal}{Journal of Development Economics}
\field{shorttitle}{Do refugee camps help or hurt hosts?}
\field{title}{Do refugee camps help or hurt hosts? The case of Kakuma, Kenya}
\field{urlday}{4}
\field{urlmonth}{6}
\field{urlyear}{2022}
\field{volume}{130}
\field{year}{2018}
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\field{pages}{66\bibrangedash 83}
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\keyw{Forced migration impacts,Household data,Nighttime lights,Price analysis,Refugee impacts}
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\field{abstract}{{OpenStreetMap} ({OSM}), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the {OSM} community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the {OSM} database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot {OSM}-{QA}-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the {OSM} community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses.}
\field{issn}{2220-9964}
\field{journaltitle}{{ISPRS} International Journal of Geo-Information}
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\field{month}{5}
\field{note}{Number: 5 Publisher: Multidisciplinary Digital Publishing Institute}
\field{number}{5}
\field{title}{Corporate Editors in the Evolving Landscape of {OpenStreetMap}}
\field{urlday}{10}
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\field{urlyear}{2022}
\field{volume}{8}
\field{year}{2019}
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\keyw{corporations,geospatial data,open data,{OpenStreetMap},Volunteered Geographic Information}
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\field{abstract}{This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks by detecting hard configurations like corners. It shows that deep learning allows to revisit remote sensing and offers promising paths for urban modeling and monitoring.}
\field{booktitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
\field{eventtitle}{2017 Joint Urban Remote Sensing Event ({JURSE})}
\field{month}{3}
\field{title}{Deep learning for urban remote sensing}
\field{year}{2017}
\field{dateera}{ce}
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\field{pages}{1\bibrangedash 4}
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\keyw{Remote sensing,Three-dimensional displays,Training,Feature extraction,Support vector machines,Labeling,Semantics}
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\field{abstract}{{<}p{>}{<}strong class="journal-{contentHeaderColor}"{>}Abstract.{<}/strong{>} The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-{RCNN} algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.{<}/p{>}}
\field{booktitle}{The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences}
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\field{eventtitle}{{XXIV} {ISPRS} Congress {<}q{>}Imaging today, foreseeing tomorrow{<}/q{>}, Commission {II} - 2021 edition, 5\–9 July 2021}
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\field{note}{{ISSN}: 1682-1750}
\field{title}{{AUTOMATIC} {OBJECT} {SEGMENTATION} {TO} {SUPPORT} {CRISIS} {MANAGEMENT} {OF} {LARGE}-{SCALE} {EVENTS}}
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\verb https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B2-2021/433/2021/
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\field{abstract}{Learning algorithms related to artificial neural networks and in particular for Deep Learning may seem to involve many bells and whistles, called hyper-parameters. This chapter is meant as a practical guide with recommendations for some of the most commonly used hyper-parameters, in particular in the context of learning algorithms based on back-propagated gradient and gradient-based optimization. It also discusses how to deal with the fact that more interesting results can be obtained when allowing one to adjust many hyper-parameters. Overall, it describes elements of the practice used to successfully and efficiently train and debug large-scale and often deep multi-layer neural networks. It closes with open questions about the training difficulties observed with deeper architectures.}
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{MIT} press Cambridge, {MA}, {USA}%
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\field{abstract}{Most machine learning algorithms are configured by one or several hyperparameters that must be carefully chosen and often considerably impact performance. To avoid a time consuming and unreproducible manual trial-and-error process to find well-performing hyperparameter configurations, various automatic hyperparameter optimization ({HPO}) methods, e.g., based on resampling error estimation for supervised machine learning, can be employed. After introducing {HPO} from a general perspective, this paper reviews important {HPO} methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. It gives practical recommendations regarding important choices to be made when conducting {HPO}, including the {HPO} algorithms themselves, performance evaluation, how to combine {HPO} with {ML} pipelines, runtime improvements, and parallelization. This work is accompanied by an appendix that contains information on specific software packages in R and Python, as well as information and recommended hyperparameter search spaces for specific learning algorithms. We also provide notebooks that demonstrate concepts from this work as supplementary files.}
\field{day}{24}
\field{eprinttype}{arxiv}
\field{journaltitle}{{arXiv}:2107.05847 [cs, stat]}
\field{month}{11}
\field{shorttitle}{Hyperparameter Optimization}
\field{title}{Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges}
\field{urlday}{16}
\field{urlmonth}{3}
\field{urlyear}{2022}
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\entry{blaschke_object_2010}{article}{}
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\field{abstract}{Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems ({GIS}). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and {GIS} functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 {GIS} and image processing started to grow together rapidly through object based image analysis ({OBIA} - or {GEOBIA} for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 {OBIA}-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the {OBIA}/{GEOBIA} developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the {OBIA} methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.}
\field{day}{1}
\field{issn}{0924-2716}
\field{journaltitle}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
\field{langid}{english}
\field{month}{1}
\field{number}{1}
\field{shortjournal}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
\field{title}{Object based image analysis for remote sensing}
\field{urlday}{10}
\field{urlmonth}{6}
\field{urlyear}{2022}
\field{volume}{65}
\field{year}{2010}
\field{dateera}{ce}
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\keyw{{GIScience},{GEOBIA},Multiscale image analysis,{OBIA},Object based image analysis}
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\field{abstract}{The amount of scientific literature on (Geographic) Object-based Image Analysis – {GEOBIA} has been and still is sharply increasing. These approaches to analysing imagery have antecedents in earlier research on image segmentation and use {GIS}-like spatial analysis within classification and feature extraction approaches. This article investigates these development and its implications and asks whether or not this is a new paradigm in remote sensing and Geographic Information Science ({GIScience}). We first discuss several limitations of prevailing per-pixel methods when applied to high resolution images. Then we explore the paradigm concept developed by Kuhn (1962) and discuss whether {GEOBIA} can be regarded as a paradigm according to this definition. We crystallize core concepts of {GEOBIA}, including the role of objects, of ontologies and the multiplicity of scales and we discuss how these conceptual developments support important methods in remote sensing such as change detection and accuracy assessment. The ramifications of the different theoretical foundations between the ‘per-pixel paradigm’ and {GEOBIA} are analysed, as are some of the challenges along this path from pixels, to objects, to geo-intelligence. Based on several paradigm indications as defined by Kuhn and based on an analysis of peer-reviewed scientific literature we conclude that {GEOBIA} is a new and evolving paradigm.}
\field{day}{1}
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\field{journaltitle}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
\field{langid}{english}
\field{month}{1}
\field{shortjournal}{{ISPRS} Journal of Photogrammetry and Remote Sensing}
\field{title}{Geographic Object-Based Image Analysis – Towards a new paradigm}
\field{urlday}{10}
\field{urlmonth}{6}
\field{urlyear}{2022}
\field{volume}{87}
\field{year}{2014}
\field{dateera}{ce}
\field{urldateera}{ce}
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\range{pages}{12}
\verb{doi}
\verb 10.1016/j.isprsjprs.2013.09.014
\endverb
\verb{file}
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\verb{urlraw}
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\verb{url}
\verb https://www.sciencedirect.com/science/article/pii/S0924271613002220
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\keyw{Image segmentation,Remote sensing,{GIScience},{GEOBIA},{OBIA},Image classification}
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family={Bolstad},
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\list{publisher}{1}{%
{Eider ({PressMinnesota})}%
}
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\field{title}{{GIS} fundamentals: A first text on geographic information systems}
\field{year}{2019}
\field{dateera}{ce}
\true{nocite}
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\field{abstract}{Task assignment methods usually rely on the fixed mean processing times of operations with the intent of balancing the workload assigned to operators or workstations in the production line. This assignment usually neglects the variability of operator processing times. In this work, a methodology in which the time in which an operator executes a task is variable, accordingly to a learning model, is proposed. It is exploited in order to assess the real-time task assignment adopted in the actual factory. The results show that, by including a learning model, it is possible to predict more accurately the long-term cycle time of the process. Standard scheduling strategies (first operator available, the operator closest to the machine) were compared with learning-oriented strategies (the most skilled, the least skilled). Through the case study, the paper addresses the problem of using a dynamic task assignment.an illustration.}
\field{day}{17}
\field{issn}{2168-1015}
\field{journaltitle}{Journal of Industrial and Production Engineering}
\field{month}{2}
\field{note}{Publisher: Taylor \& Francis \_eprint: https://doi.org/10.1080/21681015.2021.1883133}
\field{number}{2}
\field{title}{Evaluating the effect of learning rate, batch size and assignment strategies on the production performance}
\field{urlday}{14}
\field{urlmonth}{3}
\field{urlyear}{2022}
\field{volume}{38}
\field{year}{2021}
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\field{pages}{137\bibrangedash 147}
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\verb 10.1080/21681015.2021.1883133
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\verb{urlraw}
\verb https://doi.org/10.1080/21681015.2021.1883133
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\verb{url}
\verb https://doi.org/10.1080/21681015.2021.1883133
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\keyw{assembly,computer simulation,value flow improvement,Workflow balancing}
\endentry
\entry{carrivick_structure_2016}{book}{}
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family={Carrivick},
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{{hash=74980f08bbe475291ef4ad4cfeac0625}{%
family={Smith},
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{{hash=59c58bceb93b8897b1cb3ee63c5f4eac}{%
family={Quincey},
familyi={Q\bibinitperiod},
given={Duncan\bibnamedelima J},
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{John Wiley \& Sons}%
}
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\field{title}{Structure from Motion in the Geosciences}
\field{year}{2016}
\field{dateera}{ce}
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\endentry
\entry{chen_geomorphological_2020}{inproceedings}{}
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family={Chen},
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{{hash=bb3216068053c7ca6e55cf0bdcc97e25}{%
family={Scott},
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{{hash=32cc984fe3307130e37be7977a872da8}{%
family={Bearman},
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{{hash=f5e502aea480852d7e3300ecd896b759}{%
family={Anand},
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family={Keating},
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{{hash=b50b89e32cfbc2ac545d5a608b9ae0e0}{%
family={Scott},
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family={Das},
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\field{abstract}{We present a pipeline for geomorphological analysis that uses structure from motion ({SfM}) and deep learning on close-range aerial imagery to estimate spatial distributions of rock traits (size, roundness, and orientation) along a tectonic fault scarp. The properties of the rocks on the fault scarp derive from the combination of initial volcanic fracturing and subsequent tectonic and geomorphic fracturing, and our pipeline allows scientists to leverage {UAS}-based imagery to gain a better understanding of such surface processes. We start by using {SfM} on aerial imagery to produce georeferenced orthomosaics and digital elevation models ({DEM}). A human expert then annotates rocks on a set of image tiles sampled from the orthomosaics, and these annotations are used to train a deep neural network to detect and segment individual rocks in the entire site. The extracted semantic information (rock masks) on large volumes of unlabeled, high-resolution {SfM} products allows subsequent structural analysis and shape descriptors to estimate rock size, roundness, and orientation. We present results of two experiments conducted along a fault scarp in the Volcanic Tablelands near Bishop, California. We conducted the first, proof-of-concept experiment with a {DJI} Phantom 4 Pro equipped with an {RGB} camera and inspected if elevation information assisted instance segmentation from {RGB} channels. Rock-trait histograms along and across the fault scarp were obtained with the neural network inference. In the second experiment, we deployed a hexrotor and a multispectral camera to produce a {DEM} and five spectral orthomosaics in red, green, blue, red edge, and near infrared. We focused on examining the effectiveness of different combinations of input channels in instance segmentation.}
\field{booktitle}{2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
\field{eventtitle}{2020 {IEEE}/{RSJ} International Conference on Intelligent Robots and Systems ({IROS})}
\field{month}{10}
\field{note}{{ISSN}: 2153-0866}
\field{title}{Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning}
\field{year}{2020}
\field{dateera}{ce}
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\field{pages}{1276\bibrangedash 1283}
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\verb{doi}
\verb 10.1109/IROS45743.2020.9341354
\endverb
\verb{file}
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\endverb
\keyw{Neural networks,Deep learning,Cameras,Pipelines,Rocks,Structure from motion,Surface treatment}
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family={{AfDB}},
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{Brussels}%
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\field{abstract}{Secondary systems of cities in Africa ({SSCA}) have become the subject of renewed interest by scholars and international development organisations. This book explores the role played by secondary cities in the development of African countries and regions. It includes a review and discusses trends, influences, and challenges, including the forces of New Economic Geography, facing the development of secondary cities in Africa. Using a systems approach, it examines urban governance, economic, development, social, and environmental factors that have shaped the development of secondary cities. Eight country and city case studies illustrate how they have approached urbanisation, decentralisation, and other processes supporting secondary city development. Case studies include Cape Coast in Ghana, Dire Dawa in Ethiopia, Gabès in Tunisia, Huambo in Angola, Ibadan in Nigeria, Mombasa in Kenya, Gqeberha (formerly Port Elizabeth) in South Africa, and Touba‑Mbacké in Senegal. These case studies provide insights and knowledge about challenges facing the development of secondary cities within the selected countries. Learning outcomes are presented for each country case study, followed by an outline of opportunities open to secondary cities in Africa to become more competitive, dynamic, and liveable. The roles that international development assistance agencies and organisations can play to support the development of secondary systems of cities are discussed. The book concludes with a call for a new urban age agenda for the management and development of African secondary cities. This is focused on the need for improved urban governance, management, planning and economic development, and for enhancing connectivity and logistic systems to build collaborative partnerships between secondary cities and create a strong network of national systems of cities across the continent.}
\field{title}{Dynamics of Systems of Secondary Cities in Africa}
\field{year}{2022}
\field{dateera}{ce}
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\entry{congalton_assessing_2019}{book}{}
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{{CRC} press}%
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\field{title}{Assessing the accuracy of remotely sensed data: principles and practices}
\field{year}{2019}
\field{dateera}{ce}
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\field{title}{{CS}231n Convolutional Neural Networks for Visual Recognition}
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\entry{deng_imagenet_2009}{inproceedings}{}
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family={Li},
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given={Li-Jia},