scholarly journals Supervised Classification of Built-Up Areas in Sub-Saharan African Cities Using Landsat Imagery and OpenStreetMap

2018 ◽  
Vol 10 (7) ◽  
pp. 1145 ◽  
Author(s):  
Yann Forget ◽  
Catherine Linard ◽  
Marius Gilbert
Keyword(s):  
High Resolution ◽  
Supervised Learning ◽  
Spatial Data ◽  
Urban Areas ◽  
Supervised Classification ◽  
Large Scale ◽  
Landsat Imagery ◽  
Training Samples ◽  
Wide Range ◽  
Sub Saharan

The Landsat archives have been made freely available in 2008, allowing the production of high resolution built-up maps at the regional or global scale. In this context, most of the classification algorithms rely on supervised learning to tackle the heterogeneity of the urban environments. However, at a large scale, the process of collecting training samples becomes a huge project in itself. This leads to a growing interest from the remote sensing community toward Volunteered Geographic Information (VGI) projects such as OpenStreetMap (OSM). Despite the spatial heterogeneity of its contribution patterns, OSM provides an increasing amount of information on the earth’s surface. More interestingly, the community has moved beyond street mapping to collect a wider range of spatial data such as building footprints, land use, or points of interest. In this paper, we propose a classification method that makes use of OSM to automatically collect training samples for supervised learning of built-up areas. To take into account a wide range of potential issues, the approach is assessed in ten Sub-Saharan African urban areas from various demographic profiles and climates. The obtained results are compared with: (1) existing high resolution global urban maps such as the Global Human Settlement Layer (GHSL) or the Human Built-up and Settlements Extent (HBASE); and (2) a supervised classification based on manually digitized training samples. The results suggest that automated supervised classifications based on OSM can provide performances similar to manual approaches, provided that OSM training samples are sufficiently available and correctly pre-processed. Moreover, the proposed method could reach better results in the near future, given the increasing amount and variety of information in the OSM database.

scholarly journals Resource frontiers and agglomeration economies: The varied logics of transnational land-based investing in Southern and Eastern Africa

AMBIO ◽  
2022 ◽  
Author(s):  
Dilini Abeygunawardane ◽  
Angela Kronenburg García ◽  
Zhanli Sun ◽  
Daniel Müller ◽  
Almeida Sitoe ◽  
...  
Keyword(s):  
Spatial Data ◽  
Large Scale ◽  
Agglomeration Economies ◽  
Sub Saharan Africa ◽  
Eastern Africa ◽  
Track Record ◽  
Level Data ◽  
Forestry Investments ◽  
Sub Saharan ◽  
Investment Location

AbstractActor-level data on large-scale commercial agriculture in Sub-Saharan Africa are scarce. The peculiar choice of transnational investing in African land has, therefore, been subject to conjecture. Addressing this gap, we reconstructed the underlying logics of investment location choices in a Bayesian network, using firm- and actor-level interview and spatial data from 37 transnational agriculture and forestry investments across 121 sites in Mozambique, Zambia, Tanzania, and Ethiopia. We distinguish four investment locations across gradients of resource frontiers and agglomeration economies to derive the preferred locations of different investors with varied skillsets and market reach (i.e., track record). In contrast to newcomers, investors with extensive track records are more likely to expand the land use frontier, but they are also likely to survive the high transaction costs of the pre-commercial frontier. We highlight key comparative advantages of Southern and Eastern African frontiers and map the most probable categories of investment locations.

scholarly journals Dependence of Model Energy Spectra on Vertical Resolution

2016 ◽  
Vol 144 (4) ◽  
pp. 1407-1421 ◽  
Author(s):  
Michael L. Waite
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Vertical Mixing ◽  
Energy Spectra ◽  
Small Scale ◽  
Rossby Number ◽  
Vertical Resolution ◽  
Wide Range ◽  
Qualitative Shape ◽  
Vertical Grid

Abstract Many high-resolution atmospheric models can reproduce the qualitative shape of the atmospheric kinetic energy spectrum, which has a power-law slope of −3 at large horizontal scales that shallows to approximately −5/3 in the mesoscale. This paper investigates the possible dependence of model energy spectra on the vertical grid resolution. Idealized simulations forced by relaxation to a baroclinically unstable jet are performed for a wide range of vertical grid spacings Δz. Energy spectra are converged for Δz 200 m but are very sensitive to resolution with 500 m ≤ Δz ≤ 2 km. The nature of this sensitivity depends on the vertical mixing scheme. With no vertical mixing or with weak, stability-dependent mixing, the mesoscale spectra are artificially amplified by low resolution: they are shallower and extend to larger scales than in the converged simulations. By contrast, vertical hyperviscosity with fixed grid-scale damping rate has the opposite effect: underresolved spectra are spuriously steepened. High-resolution spectra are converged except for the stability-dependent mixing case, which are damped by excessive mixing due to enhanced shear over a wide range of horizontal scales. It is shown that converged spectra require resolution of all vertical scales associated with the resolved horizontal structures: these include quasigeostrophic scales for large-scale motions with small Rossby number and the buoyancy scale for small-scale motions at large Rossby number. It is speculated that some model energy spectra may be contaminated by low vertical resolution, and it is recommended that vertical-resolution sensitivity tests always be performed.

scholarly journals Platanus_B: an accurate de novo assembler for bacterial genomes using an iterative error-removal process

DNA Research ◽  
2020 ◽  
Vol 27 (3) ◽  
Author(s):  
Rei Kajitani ◽  
Dai Yoshimura ◽  
Yoshitoshi Ogura ◽  
Yasuhiro Gotoh ◽  
Tetsuya Hayashi ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
De Novo ◽  
Bacterial Genomes ◽  
Long Reads ◽  
Wide Range ◽  
Long Read ◽  
Phylogenomic Analyses ◽  
Error Removal ◽  
Iterative Error

Abstract De novo assembly of short DNA reads remains an essential technology, especially for large-scale projects and high-resolution variant analyses in epidemiology. However, the existing tools often lack sufficient accuracy required to compare closely related strains. To facilitate such studies on bacterial genomes, we developed Platanus_B, a de novo assembler that employs iterations of multiple error-removal algorithms. The benchmarks demonstrated the superior accuracy and high contiguity of Platanus_B, in addition to its ability to enhance the hybrid assembly of both short and nanopore long reads. Although the hybrid strategies for short and long reads were effective in achieving near full-length genomes, we found that short-read-only assemblies generated with Platanus_B were sufficient to obtain ≥90% of exact coding sequences in most cases. In addition, while nanopore long-read-only assemblies lacked fine-scale accuracies, inclusion of short reads was effective in improving the accuracies. Platanus_B can, therefore, be used for comprehensive genomic surveillances of bacterial pathogens and high-resolution phylogenomic analyses of a wide range of bacteria.

scholarly journals Urbanization and Increasing Flood Risk in the Northern Coast of Central Java—Indonesia: An Assessment towards Better Land Use Policy and Flood Management

Land ◽  
2020 ◽  
Vol 9 (10) ◽  
pp. 343
Author(s):  
Wiwandari Handayani ◽  
Uchendu Eugene Chigbu ◽  
Iwan Rudiarto ◽  
Intan Hapsari Surya Putri
Keyword(s):  
Land Use ◽  
River Basin ◽  
Spatial Data ◽  
Urban Areas ◽  
Landsat Imagery ◽  
Flood Management ◽  
Spatial Data Analysis ◽  
Northern Coast ◽  
Flood Events ◽  
Central Java

This study explores urbanization and flood events in the northern coast of Central Java with river basin as its unit of analysis. Two types of analysis were applied (i.e., spatial data and non-spatial data analysis) at four river basin areas in Central Java—Indonesia. The spatial analysis is focused on the assessment of LULC change in 2009–2018 based on Landsat Imagery. The non-spatial data (i.e., rural-urban classification and flood events) were overlaid with results of spatial data analyses. Our findings show that urbanization, as indicated by the growth rate of built-up areas, is very significant. Notable exposure to flood has taken place in the urban and potentially urban areas. The emerging discussion indicates that river basins possess dual spatial identity in the urban system (policy- and land-use-related). Proper land use planning and control is an essential instrument to safeguard urban areas (such as the case study area) and the entire island of Java in Indonesia. More attention should be put upon the river basin areas in designing eco-based approach to tackle the urban flood crises. In this case, the role of governance in flood management is crucial.

scholarly journals RADAR-Base: Open Source Mobile Health Platform for Collecting, Monitoring, and Analyzing Data Using Sensors, Wearables, and Mobile Devices

10.2196/11734 ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. e11734 ◽  
Author(s):  
Yatharth Ranjan ◽  
Zulqarnain Rashid ◽  
Callum Stewart ◽  
Pauline Conde ◽  
Mark Begale ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Collection ◽  
Open Source ◽  
Mobile Health ◽  
Large Scale ◽  
Streaming Data ◽  
Security And Privacy ◽  
The European Union ◽  
Wide Range ◽  
Remote Assessment

Background With a wide range of use cases in both research and clinical domains, collecting continuous mobile health (mHealth) streaming data from multiple sources in a secure, highly scalable, and extensible platform is of high interest to the open source mHealth community. The European Union Innovative Medicines Initiative Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) program is an exemplary project with the requirements to support the collection of high-resolution data at scale; as such, the Remote Assessment of Disease and Relapse (RADAR)-base platform is designed to meet these needs and additionally facilitate a new generation of mHealth projects in this nascent field. Objective Wide-bandwidth networks, smartphone penetrance, and wearable sensors offer new possibilities for collecting near-real-time high-resolution datasets from large numbers of participants. The aim of this study was to build a platform that would cater for large-scale data collection for remote monitoring initiatives. Key criteria are around scalability, extensibility, security, and privacy. Methods RADAR-base is developed as a modular application; the backend is built on a backbone of the highly successful Confluent/Apache Kafka framework for streaming data. To facilitate scaling and ease of deployment, we use Docker containers to package the components of the platform. RADAR-base provides 2 main mobile apps for data collection, a Passive App and an Active App. Other third-Party Apps and sensors are easily integrated into the platform. Management user interfaces to support data collection and enrolment are also provided. Results General principles of the platform components and design of RADAR-base are presented here, with examples of the types of data currently being collected from devices used in RADAR-CNS projects: Multiple Sclerosis, Epilepsy, and Depression cohorts. Conclusions RADAR-base is a fully functional, remote data collection platform built around Confluent/Apache Kafka and provides off-the-shelf components for projects interested in collecting mHealth datasets at scale.

scholarly journals Profiles of Galaxy Clusters in Cosmological Scenarios

1987 ◽  
Vol 117 ◽  
pp. 287-287
Author(s):  
Michael J. West ◽  
Avishai Dekel ◽  
Augustus Oemler
Keyword(s):  
High Resolution ◽  
Galaxy Clusters ◽  
Hierarchical Clustering ◽  
Large Scale ◽  
Power Spectra ◽  
Clusters Of Galaxies ◽  
Spatial Symmetry ◽  
Abell Clusters ◽  
Wide Range ◽  
Large Scale Simulations

We have studied the properties of rich clusters of galaxies in various cosmological scenarios by comparing high resolution N-body simulations with observations of Abell clusters. The clusters have been simulated in two steps. First, protoclusters are identified in large-scale simulations which represent a wide range of cosmological scenarios (hierarchical clustering, pancake scenarios, and hybrids of the two, spanning a range of power spectra). Then the region around each protocluster is simulated with high resolution, the particles representing L* galaxies. The protoclusters have no spatial symmetry built into them initially. The final clusters are still dynamically young, and of moderate densities, which should be representative of Abell clusters of richness classes 1 and 2.

Image/Video Semantic Analysis by Semi-Supervised Learning

2011 ◽  
pp. 183-210
Author(s):  
Jinhui Tang ◽  
Xian-Sheng Hua ◽  
Meng Wang
Keyword(s):  
Supervised Learning ◽  
Large Scale ◽  
Semantic Analysis ◽  
Image Annotation ◽  
Structural Difference ◽  
Video Annotation ◽  
Learning Methods ◽  
Manifold Ranking ◽  
Training Samples ◽  
Structure Sensitive

The insufficiency of labeled training samples is a major obstacle in automatic semantic analysis of large scale image/video database. Semi-supervised learning, which attempts to learn from both labeled and unlabeled data, is a promising approach to tackle this problem. As a major family of semi-supervised learning, graph-based methods have attracted more and more recent research. In this chapter, a brief introduction is given on popular semi-supervised learning methods, especially the graph-based methods, as well as their applications in the area of image annotation, video annotation, and image retrieval. It is well known that the pair-wise similarity is an essential factor in graph propagation based semisupervised learning methods. A novel graph-based semi-supervised learning method, named Structure- Sensitive Anisotropic Manifold Ranking (SSAniMR), is derived from a PDE based anisotropic diffusion framework. Instead of using Euclidean distance only, SSAniMR further takes local structural difference into account to more accurately measure pair-wise similarity. Finally some future directions of using semi-supervised learning to analyze the multimedia content are discussed.

scholarly journals Feature Investigation for Large Scale Urban Detection Using Landsat Imagery

Proceedings ◽  
2018 ◽  
Vol 2 (7) ◽  
pp. 349
Author(s):  
Fathalrahman Adam ◽  
Thomas Esch ◽  
Mihai Datcu
Keyword(s):  
Urban Areas ◽  
Large Scale ◽  
Landsat Imagery ◽  
Support Vector ◽  
Relative Importance ◽  
Importance Score ◽  
Ranking Methods ◽  
Common Features ◽  
Np Hard Problem ◽  
The Individual

Many works dealing with the problem of urban detection at large scale have been published, but very little attention has been paid to the investigation of the features’ relative importance. Feature selection is known to be an NP-hard problem, which means it can not be solved in polynomial time, but there are many heuristics suggested to approximate the solution. In this paper, a survey of the features used for large scale urban detection is presented, then the question of finding the best subset of features is investigated. Using Landsat scenes of five urban areas, most common features were extracted to represent the full feature set. Employing mutual information based ranking methods, Support Vector Machine (SVM) and Random Forest feature ranking, an importance score was assigned to each feature by each method. To aggregate the individual rankings of features, a two stage voting scheme was implemented to choose a subset of size N as the most relevant features. The most important features for all five cities taken together were listed.

scholarly journals Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

2019 ◽  
Vol 11 (11) ◽  
pp. 1343 ◽  
Author(s):  
Shunping Ji ◽  
Yanyun Shen ◽  
Meng Lu ◽  
Yongjun Zhang
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Large Scale ◽  
Real Life ◽  
Aerial Images ◽  
Building Extraction ◽  
Object Based ◽  
Wide Range ◽  
Change Map

We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm’s robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods—including the most recent CNN-based and generative adversarial network (GAN)-based ones—have only reached 25% AP in their best cases.

scholarly journals Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

2019 ◽  
Vol 11 (1) ◽  
pp. 88 ◽  
Author(s):  
Nianxue Luo ◽  
Taili Wan ◽  
Huaixu Hao ◽  
Qikai Lu
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Spatial Information ◽  
Land Cover Classification ◽  
Remotely Sensed ◽  
Geographical Information ◽  
Remotely Sensed Imagery ◽  
Training Samples ◽  
Comparison Results

Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches.

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