scholarly journals Clasificación espacial del suelo urbano por el valor especulativo del suelo e imágenes MSI satelitales usando K-MEANS, Huancayo, Perú

2021 ◽  
Vol 24 (44) ◽  
pp. 70-83
Author(s):  
Gonzalo Rodolfo Peña-Zamalloa
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Land Use Changes ◽  
Land Market ◽  
Objective Classification ◽  
Urban Land Market ◽  
The City ◽  
Sentinel 2 ◽  
Urban Territory

The city of Huancayo, like other intermediate cities in Latin America, faces problems of poorly planned land-use changes and a rapid dynamic of the urban land market. The scarce and outdated information on the urban territory impedes the adequate classification of urban areas, limiting the form of its intervention. The purpose of this research was the adoption of unassisted and mixed methods for the spatial classification of urban areas, considering the speculative land value, the proportion of urbanized land, and other geospatial variables. Among the data collection media, Multi-Spectral Imagery (MSI) from the Sentinel-2 satellite, the primary road system, and a sample of direct observation points, were used. The processed data were incorporated into georeferenced maps, to which urban limits and official slopes were added. During data processing, the K-Means algorithm was used, together with other machine learning and assisted judgment methods. As a result, an objective classification of urban areas was obtained, which differs from the existing planning.

scholarly journals Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

2021 ◽  
Vol 13 (9) ◽  
pp. 4728
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Rural Communities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Subsistence Agriculture ◽  
Smallholder Farms ◽  
Main Driver ◽  
Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

Urban land market and land-use changes in post-reform China: A case study of Beijing

2014 ◽  
Vol 124 ◽  
pp. 118-128 ◽  
Author(s):  
Jinfeng Du ◽  
Jean-Claude Thill ◽  
Richard B. Peiser ◽  
Changchun Feng
Keyword(s):  
Land Use ◽  
Land Use Changes ◽  
Urban Land ◽  
Land Market ◽  
Urban Land Market

scholarly journals Study on Land-use Changes and Their Impacts on Air Pollution in Chengdu

Atmosphere ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 42 ◽  
Author(s):  
Wei Sun ◽  
Zhihong Liu ◽  
Yang Zhang ◽  
Weixin Xu ◽  
Xiaotong Lv ◽  
...  
Keyword(s):  
Land Use ◽  
Wind Speed ◽  
Correlation Coefficient ◽  
Urban Areas ◽  
Land Use Changes ◽  
Urban Land ◽  
Urban Land Use ◽  
Simulation Results ◽  
The City ◽  
Better Than

The expansion of urban areas and the increase in the number of buildings and urbanization characteristics, such as roads, affect the meteorological environment in urban areas, resulting in weakened pollutant dispersion. First, this paper uses GIS (geographic information system) spatial analysis technology and landscape ecology analysis methods to analyze the dynamic changes in land cover and landscape patterns in Chengdu as a result of urban development. Second, the most appropriate WRF (Weather Research and Forecasting) model parameterization scheme is selected and screened. Land-use data from different development stages in the city are included in the model, and the wind speed and temperature results simulated using new and old land-use data (1980 and 2015) are evaluated and compared. Finally, the results of the numerical simulations by the WRF-Chem air quality model using new and old land-use data are coupled with 0.25° × 0.25°-resolution MEIC (Multi-resolution Emission Inventory for China) emission source data from Tsinghua University. The results of the sensitivity experiments using the WRF-Chem model for the city under different development conditions and during different periods are discussed. The meteorological conditions and pollution sources remained unchanged as the land-use data changed, which revealed the impact of urban land-use changes on the simulation results of PM2.5 atmospheric pollutants. The results show the following. (1) From 1980 to 2015, the land-use changes in Chengdu were obvious, and cultivated land exhibited the greatest changes, followed by forestland. Under the influence of urban land-use dynamics and human activities, both the richness and evenness of the landscape in Chengdu increased. (2) The microphysical scheme WSM3 (WRF Single–Moment 3 class) and land-surface scheme SLAB (5-layer diffusion scheme) were the most suitable for simulating temperatures and wind speeds in the WRF model. The wind speed and temperature simulation results using the 2015 land-use data were better than those using the 1980 land-use data when assessed according to the coincidence index and correlation coefficient. (3) The WRF-Chem simulation results obtained for PM2.5 using the 2015 land-use data were better than those obtained using the 1980 land-use data in terms of the correlation coefficient and standard deviation. The concentration of PM2.5 in urban areas was higher than that in the suburbs, and the concentration of PM2.5 was lower on Longquan Mountain in Chengdu than in the surrounding areas.

Objective classification of changes in water regime types of the Russian Plain rivers utilizing machine learning approaches

2020 ◽  
Author(s):  
Alexander Ivanov ◽  
Timophey Samsonov ◽  
Natalia Frolova ◽  
Maria Kireeva ◽  
Elena Povalishnikova
Keyword(s):  
Machine Learning ◽  
Water Regime ◽  
Cosine Similarity ◽  
Regime Type ◽  
Russian Plain ◽  
Regime Types ◽  
Objective Classification ◽  
Seasonal Runoff ◽  
Monthly Runoff

<p>Hydrological regime classification of Russian Plain rivers was always done by hand and by using subjective analysis of various characteristics of a seasonal runoff. Last update to this classification was made in the early 1990s. </p><p>In this work we make an attempt at using different machine learning methods for objective classification. Both clustering (DBSCAN, K-Means) and classification (XGBoost) methods were used to establish 1) if an established runoff types can be inferred from the data using supervised approach 2) similar clusters can be inferred from data (unsupervised approach). Monthly runoff data for 237 rivers of Russian Plain since 1945 and until 2016 were used as a dataset. </p><p>In a first attempt dataset was divided into periods of 1945-1977 and 1978-2016 in attempt to detect changes in river water regimes due to climate change. Monthly data were transformed into following features: annual and seasonal runoff, runoff levels for different seasons, minimum and maximum values of monthly runoff, ratios of the minimum and maximum runoff compared to yearly average and others. Supervised classification using XGBoost method resulted in 90% accuracy in water regime type identification for 1945-1977 period. Shifts in water regime types for southern rivers of Russian Plain rivers in a Don region were identified by this classifier.</p><p>DBSCAN algorithm for clustering was able to identify 6 major clusters corresponding to existing water regime types: Kola peninsula, North-East part of Russian Plain and polar Urals, Central Russia, Southern Russia, arid South-East, foothills and separately higher altitudes of the Caucasus. Nonetheless a better approach was sought due to intersections of a clusters because of the continuous nature of data. Cosine similarity metric was used as an alternative way to separate river runoff types, this time for each year. Yearly cutoff also allows us to make a timeline of water regime changes over the course of 70 years. By using it as an objective ground truth we plan to remake classification and clusterization made earlier and establish an automated way to classify changes in water regime over time.</p><p><strong>As a result, the following conclusions can be made</strong></p><ol><li>It’s possible to train an accurate classifier based on established water regime type and apply it to detect changes in water regime types over the course of time</li> <li>By applying the classifier to different periods of time we can detect a shift to “southern” type of water regime in the central area of Russian Plain</li> <li>Despite the highly continuous nature of data it seems possible to use cosine similarity metric to separate water regime types into zones corresponding to established ones</li> </ol><p><span><em>The study was supported by the Russian Science Foundation (grant No.19-77-10032) in methods </em><em>and Russian Foundation for Basic Research (grant No.18-05-60021</em>) </span><em><span>for analyses in Arctic region </span></em></p>

scholarly journals New urban area flood model: a comparison with MIKE11-quasi2d

2005 ◽  
Vol 2 ◽  
pp. 279-284 ◽  
Author(s):  
A. Sole ◽  
G. Zuccaro
Keyword(s):  
Urban Areas ◽  
Explicit Scheme ◽  
The Other ◽  
Flood Wave ◽  
Piezometric Level ◽  
Simulated Event ◽  
The City ◽  
Water Overflow ◽  
Field Area ◽  
Urban Territory

Abstract. Recent hydrogeological events have increased both public interest and that of the Scientific Community in a more accurate study of flooding in urban areas. The present project proposes a new model which offers an optimal integration of two models, one for flood wave propagation in riverbeds and the other for flooding in urban areas. We consider it necessary to not only treat the modelling of the outflow in riverbeds and outside riverbeds.together but to integrate them thoroughly. We simulate the propagation in riverbed of the flood event with a model solving the equations of De Saint Venant with the explicit scheme at the finite differences by McCormack. The propagation outside the riverbed is simulated using an algorithm proposed by Braschi et al. (1990). This algorithm is based on a local discretization of the urban territory, divided in a series of "tanks" and "channels". Each tank is associated with an area of an extension related to the position of the other tanks and the quantity of buildings, modelled as insurmountable obstacles. The model facilitates the simultaneous performance of the two simulations: at each instant, the quantitiy of water overflow, depending on the piezometric level in every section, is calculated as a function of the dimensions of the weirs (the banks), assuming it passes through the critical state. Then, it is transferred to the tanks placed in the surroundings of the overflow points. Those points are the starting nodes for the propagation of the flood because they are connected to the network of tanks in which the surrounding land has been schematised. In this paper, we present a comparison of one of the most powerful models of inundation simulation in urban and no-urban areas. The field area is the city of Albenga (SV, Italy) and the simulated event is the inundation of the 1994 (return period of about 25 years).

scholarly journals Reliable Crops Classification Using Limited Number of Sentinel-2 and Sentinel-1 Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3176
Author(s):  
Beata Hejmanowska ◽  
Piotr Kramarczyk ◽  
Ewa Głowienka ◽  
Sławomir Mikrut
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Confusion Matrix ◽  
High Accuracy ◽  
Training Set ◽  
Validation Data ◽  
Comparative Accuracy ◽  
The Difference ◽  
Sentinel 2

The study presents the analysis of the possible use of limited number of the Sentinel-2 and Sentinel-1 to check if crop declarations that the EU farmers submit to receive subsidies are true. The declarations used in the research were randomly divided into two independent sets (training and test). Based on the training set, supervised classification of both single images and their combinations was performed using random forest algorithm in SNAP (ESA) and our own Python scripts. A comparative accuracy analysis was performed on the basis of two forms of confusion matrix (full confusion matrix commonly used in remote sensing and binary confusion matrix used in machine learning) and various accuracy metrics (overall accuracy, accuracy, specificity, sensitivity, etc.). The highest overall accuracy (81%) was obtained in the simultaneous classification of multitemporal images (three Sentinel-2 and one Sentinel-1). An unexpectedly high accuracy (79%) was achieved in the classification of one Sentinel-2 image at the end of May 2018. Noteworthy is the fact that the accuracy of the random forest method trained on the entire training set is equal 80% while using the sampling method ca. 50%. Based on the analysis of various accuracy metrics, it can be concluded that the metrics used in machine learning, for example: specificity and accuracy, are always higher then the overall accuracy. These metrics should be used with caution, because unlike the overall accuracy, to calculate these metrics, not only true positives but also false positives are used as positive results, giving the impression of higher accuracy. Correct calculation of overall accuracy values is essential for comparative analyzes. Reporting the mean accuracy value for the classes as overall accuracy gives a false impression of high accuracy. In our case, the difference was 10–16% for the validation data, and 25–45% for the test data.

Classification of Traffic Events in Mexico City Using Machine Learning and Volunteered Geographic Information

2019 ◽  
pp. 141-162
Author(s):  
Magdalena Saldana-Perez ◽  
Miguel Torres-Ruiz ◽  
Marco Moreno-Ibarra
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Learning Algorithm ◽  
Life Quality ◽  
Real Data ◽  
Geographic Information ◽  
User Generated Content ◽  
Urban Problem ◽  
Internet Information

Volunteer geographic information and user-generated content represents a source of updated information about what people perceive from their environment. Its analysis generates the opportunity to develop processes to study and solve social problems that affect the people's lives, merging technology and real data. One of the problems in urban areas is the traffic. Every day at big cities people lose time, money, and life quality when they get stuck in traffic jams; another urban problem derived from traffic is air pollution. In the present approach, a traffic event classification methodology is implemented to analyze VGI and internet information related to traffic events with a view to identify the main traffic problems in a city and to visualize the congested roads. The methodology uses different computing tools and algorithms to achieve the goal. To obtain the data, a social media and RSS channels are consulted. The extracted data texts are classified into seven possible traffic events, and geolocalized. In the classification, a machine learning algorithm is applied.

scholarly journals A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images

2019 ◽  
Vol 11 (13) ◽  
pp. 1600 ◽  
Author(s):  
Flávio F. Camargo ◽  
Edson E. Sano ◽  
Cláudia M. Almeida ◽  
José C. Mura ◽  
Tati Almeida
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Tropical Savanna ◽  
Learning Techniques ◽  
Bare Soils

This study proposes a workflow for land use and land cover (LULC) classification of Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) images of the Brazilian tropical savanna (Cerrado) biome. The following LULC classes were considered: forestlands; shrublands; grasslands; reforestations; croplands; pasturelands; bare soils/straws; urban areas; and water reservoirs. The proposed approach combines polarimetric attributes, image segmentation, and machine-learning procedures. A set of 125 attributes was generated using polarimetric ALOS-2/PALSAR-2 images, including the van Zyl, Freeman–Durden, Yamaguchi, and Cloude–Pottier target decomposition components, incoherent polarimetric parameters (biomass indices and polarization ratios), and HH-, HV-, VH-, and VV-polarized amplitude images. These attributes were classified using the Naive Bayes (NB), DT J48 (DT = decision tree), Random Forest (RF), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) algorithms. The RF, MLP, and SVM classifiers presented the most accurate performances. NB and DT J48 classifiers showed a lower performance in relation to the RF, MLP, and SVM. The DT J48 classifier was the most suitable algorithm for discriminating urban areas and natural vegetation cover. The proposed workflow can be replicated for other SAR images with different acquisition modes or for other types of vegetation domains.

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