Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

Grippa, Taïs; Georganos, Stefanos; Zarougui, Soukaina; Bognounou, Pauline; Diboulo, Eric; Forget, Yann; Lennert, Moritz; Vanhuysse, Sabine; Mboga, Nicholus; Wolff, Éléonore

doi:10.3390/ijgi7070246

Cited by 83 publications

(66 citation statements)

References 56 publications

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“…Land cover mapping are among the most important application of earth observation satellites sensor data. Change of the land cover may affect the climate through manipulation of the composition of pollutant emissions like carbon dioxide [7,8]. Today, LULC statistics are the prerequisites for policy and decision making strategies which have an effect on societies and their economies [9].…”

Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Jamali

2019

SN Appl. Sci.

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Land use land cover change mapping has been used for monitoring environmental changes as an essential factor to study on the earth's surface land cover in the field of climate change phenomena such as floods and droughts. Remote sensing images have been suggested to present inexpensive and fine-scale data offering multi-temporal coverage. This tool is useful in the field of environmental monitoring, land-cover mapping, and urban planning. This study aims to evaluate eight machine learning algorithms for image classification implemented in WEKA and R programming language. Firstly, Landsat 8 OLI/TIRS Level-2 images based on eight machine learning techniques including Random Forest, Decision Table, DTNB, J48, Lazy IBK, Multilayer Perceptron, Non-Nested Generalized Exemplars (NN ge), and Simple Logistic are classified. Then, obtained results are compared in term of Overall Accuracy (OA), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE) for land use land cover mapping. Among the eight machine learning algorithms used for image classification based on the training and test dataset, NN ge classifier is ranked first with values of 100, 0, and 0 for Overall Accuracy, Mean Absolute Error and Root Mean Squared Error respectively. All machine learning algorithms had an Overall Accuracy of more than 99% for the training dataset. On the other hand, for the test dataset, J48 and DTNB algorithms had the worst performance with values of 88.1188 and 76.9802 respectively for the Overall Accuracy.

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Section: Introductionmentioning

confidence: 99%

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Jamali

2019

SN Appl. Sci.

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“…The land use is predicted at the city block level [103], providing sufficient spatial detail, suitable for mapping urban functions. However, most of the time, city blocks do not exist in LMICs.…”

Section: The Potential Of Obia For Generating Land Cover Information mentioning

confidence: 99%

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

et al. 2020

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Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

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“…In the last years, additional GIS information has been incrementally employed to support land cover classification. In (Grippa et al, 2018), OpenStreetMap data is used as additional landuse information, for segment-based landuse mapping, despite the fact that in some areas OSM data may be incomplete and incorrect. Furthermore, OSM data is also used in (Lopes et al, 2017), supporting special LCZ-based (Local Urban Zones) classes.…”

Section: Related Workmentioning

confidence: 99%

Statistical Analysis of Airborne Imagery Combined With Gis Information for Training Data Generation

Häufel

Bulatov

Helmholz

2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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In recent years, the task of land cover classification from airborne image and elevation data advanced considerably due to enhanced applicability of CNNs (Convolutional Neural Networks). Nevertheless, CNNs require a huge amount of training data. Traditionally, few essential feature values, such as elevation or vegetation index, had been chosen to provide a coarse distinction of classes, but very often these values have to be adapted depending on the imagery. To improve this process, freely available GIS data are combined with spectral and spatial features (and their variations) following the K-Means and Mean-Shift algorithm. Based on cluster assignments to pixels, statistical analysis for extracting plausible values for distinguishing between land cover classes is applied. The resulting labeled databases are evaluated using ground truth data, and will form the basis for the training data required for CNNs.

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Mapping Urban Land Use at Street Block Level Using OpenStreetMap, Remote Sensing Data, and Spatial Metrics

Cited by 83 publications

References 56 publications

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

Evaluation and comparison of eight machine learning models in land use/land cover mapping using Landsat 8 OLI: a case study of the northern region of Iran

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

Statistical Analysis of Airborne Imagery Combined With Gis Information for Training Data Generation

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