Innovative methodology of demand responsive approach for large-scale water supply and sewerage/on-site sanitation projects in developing countries using participatory GIS with high resolution satellite imagery

2011 ◽  
Vol 60 (5) ◽  
pp. 294
Author(s):  
Shozo Mori ◽  
Ihsanullah ◽  
Akira Koizumi ◽  
Chinatsu Maeda ◽  
Kazuhiro Asada ◽  
...  
Keyword(s):  
Developing Countries ◽  
High Resolution ◽  
Water Supply ◽  
Satellite Imagery ◽  
Large Scale ◽  
Participatory Gis ◽  
Innovative Methodology ◽  
High Resolution Satellite Imagery

scholarly journals Penataan Batas Wilayah Administrasi Desa, Hambatan dan Alternatif Solusi dengan Pendekatan Geospasial

1969 ◽  
Vol 12 (2) ◽  
pp. 131-147
Author(s):  
Asadi Asadi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
High Resolution Satellite Imagery ◽  
Main Instrument ◽  
Topographical Maps ◽  
Imagery Data

Law No. 6 of 2014 concerning Villages provides additional evidence that Indonesia has paid more attention and respect to the existence of villages. The significant amount of village expansion lately is not matched with the clarity of village boundaries that may rise in to potential conflicts. Ideally, the entire instruments to structure village boundaries must first be prepared. One of the instruments needed is the availability of large scale of basic maps (topographical maps) as the main instrument of making a village map. Unfortunately, the large-scale topographical maps are not available yet. This paper provides an alternative acceleration of village boundaries arrangement using High Resolution Satellite Imagery Data that has passed orthorectified process. By involving the community and village leaders in the process of structuring boundaries, and supported by the spirit of fraternity, all problems occured during the activity of village boundaries can be solved with the very best solution.Keywords: village boundary, High Resolution Satellite Imagery Data, spirit of fraternityUndang-Undang Nomor 6 Tahun 2014 tentang Desa memberikan tambahan bukti bahwa negara semakin memperhatikan dan menghormati keberadaan desa. Adanya pemekaran wilayah desa yang signifikan akhir-akhir ini, tidak diimbangi dengan kejelasan batas wilayah desa,berpotensi menimbulkan konflik. Idealnya, seluruh instrumen untuk melakukan penataan batas wilayah desa harus terlebih dahulu disiapkan. Salah satu instrumen tersebut adalah tersedianya peta dasar (peta rupabumi) skala besar sebagai bahan utama pembuatan peta desa. Sayangnya ketersediaan peta rupabumi skala besar belum tersedia. Tulisan ini memberikan alternatif percepatan penataan batas wilayah desa yang dapat menggunakan Citra Satelit Resolusi Tinggi (CSRT) yang sudah melalui proses ortorektifikasi. Dengan melibatkan masyarakat dan tokoh masyarakat desa dalam melakukan proses penataan batas wilayah, dan dengan didukung semangat persaudaraan, diharapkan permasalahan batas wilayah desa dapat diselesaikan dengan sebaik-baiknya.Kata kunci: batas desa, metode kartometrik, CSRT, semangat persaudaraan

scholarly journals Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

2020 ◽  
Vol 12 (7) ◽  
pp. 1213 ◽  
Author(s):  
Muhammad M. Raza ◽  
Chris Harding ◽  
Matt Liebman ◽  
Leonor F. Leandro
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Sudden Death ◽  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Sudden Death Syndrome ◽  
Alternative Methods ◽  
High Resolution Satellite Imagery ◽  
Soybean Diseases

Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.

scholarly journals Mapping Large-Scale Plateau Forest in Sanjiangyuan Using High-Resolution Satellite Imagery and Few-Shot Learning

2022 ◽  
Vol 14 (2) ◽  
pp. 388
Author(s):  
Zhihao Wei ◽  
Kebin Jia ◽  
Xiaowei Jia ◽  
Pengyu Liu ◽  
Ying Ma ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Ground Truth ◽  
Fine Tuning ◽  
Forest Monitoring ◽  
Learning Method ◽  
Ground Truth Data ◽  
Sensing Applications ◽  
High Resolution Satellite Imagery

Monitoring the extent of plateau forests has drawn much attention from governments given the fact that the plateau forests play a key role in global carbon circulation. Despite the recent advances in the remote-sensing applications of satellite imagery over large regions, accurate mapping of plateau forest remains challenging due to limited ground truth information and high uncertainties in their spatial distribution. In this paper, we aim to generate a better segmentation map for plateau forests using high-resolution satellite imagery with limited ground-truth data. We present the first 2 m spatial resolution large-scale plateau forest dataset of Sanjiangyuan National Nature Reserve, including 38,708 plateau forest imagery samples and 1187 handmade accurate plateau forest ground truth masks. We then propose an few-shot learning method for mapping plateau forests. The proposed method is conducted in two stages, including unsupervised feature extraction by leveraging domain knowledge, and model fine-tuning using limited ground truth data. The proposed few-shot learning method reached an F1-score of 84.23%, and outperformed the state-of-the-art object segmentation methods. The result proves the proposed few-shot learning model could help large-scale plateau forest monitoring. The dataset proposed in this paper will soon be available online for the public.

scholarly journals Large-scale monitoring of coppice forest clearcuts by multitemporal very high resolution satellite imagery. A case study from central Italy

2011 ◽  
Vol 115 (4) ◽  
pp. 1025-1033 ◽  
Author(s):  
Gherardo Chirici ◽  
Diego Giuliarelli ◽  
Daniele Biscontini ◽  
Daniela Tonti ◽  
Walter Mattioli ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Central Italy ◽  
High Resolution Satellite Imagery ◽  
Coppice Forest ◽  
Very High

scholarly journals COMPARATIVE ASSESSMENT OF VERY HIGH RESOLUTION SATELLITE AND AERIAL ORTHOIMAGERY

2015 ◽  
Vol XL-3/W2 ◽  
pp. 1-7 ◽  
Author(s):  
P. Agrafiotis ◽  
A. Georgopoulos
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Best Practice ◽  
Single Point ◽  
Critical Approach ◽  
Qualitative Properties ◽  
Check Points ◽  
High Resolution Satellite Imagery

This paper aims to assess the accuracy and radiometric quality of orthorectified high resolution satellite imagery from Pleiades-1B satellites through a comparative evaluation of their quantitative and qualitative properties. A Pleiades-B1 stereopair of high resolution images taken in 2013, two adjacent GeoEye-1 stereopairs from 2011 and aerial orthomosaic (LSO) provided by NCMA S.A (Hellenic Cadastre) from 2007 have been used for the comparison tests. As control dataset orthomosaic from aerial imagery provided also by NCMA S.A (0.25m GSD) from 2012 was selected. The process for DSM and orthoimage production was performed using commercial digital photogrammetric workstations. The two resulting orthoimages and the aerial orthomosaic (LSO) were relatively and absolutely evaluated for their quantitative and qualitative properties. Test measurements were performed using the same check points in order to establish their accuracy both as far as the single point coordinates as well as their distances are concerned. Check points were distributed according to JRC Guidelines for Best Practice and Quality Checking of Ortho Imagery and NSSDA standards while areas with different terrain relief and land cover were also included. The tests performed were based also on JRC and NSSDA accuracy standards. Finally, tests were carried out in order to assess the radiometric quality of the orthoimagery. The results are presented with a statistical analysis and they are evaluated in order to present the merits and demerits of the imaging sensors involved for orthoimage production. The results also serve for a critical approach for the usability and cost efficiency of satellite imagery for the production of Large Scale Orthophotos.

Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries

2021 ◽  
Author(s):  
Andrew Griffin ◽  
Sean Griffin ◽  
Kristofer Lasko ◽  
Megan Maloney ◽  
S. Blundell ◽  
...  
Keyword(s):  
Developing Countries ◽  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Network Connectivity ◽  
Automated Feature Extraction ◽  
Before And After ◽  
High Resolution Satellite Imagery

Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

VERY HIGH RESOLUTION SATELLITE IMAGERY UTILIZATION STANDARD FOR LARGE SCALE TOPOGRAPHIC MAPPING

2021 ◽  
pp. 939
Author(s):  
Winhard Tampubolon ◽  
Wolfgang Reinhardt ◽  
Franz Josef Behr
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Large Scale ◽  
Local Coordinate System ◽  
Control Point ◽  
Least Square ◽  
Topographic Mapping ◽  
Geometric Correction ◽  
High Resolution Satellite Imagery ◽  
Very High

Due to its large area Large Scale Topographic Mapping (LSTM) for Indonesia requires acceleration strategies that must be innovative enough to take into account the production efficiency. Satellite-based technologies are still a preferable choice especially in conjunction with the security clearance and weather. Standards for the Very High-Resolution Satellite Imagery (VHRS) utilization are essential, especially in a situation where there are so many available sensors and processing methods implemented. Hence, the selection of a proper geometric correction method is fundamental in order to utilize the VHRS imagery as one source of geospatial data especially for LSTM production and updating purposes. For CSRT geometric correction, an orthorectification process is required, where this process requires input data from the Ground Control Point (TKT) and the Digital Elevation Model (DEM). Therefore, the Least Square Adjustment (LSA) method is implemented to be able to include 8-9 GCPs per-scene (orbital and sensor parameters) and the DEM with a maximum resolution 4 times of the VHRS imagery’s Ground Sampling Distance (GSD) in the process of producing VHRS orthoimages. In addition, the role of orbital and sensor parameters is also essential for the geometric correction because its relation to the Direct Georeferencing (DG) of each pixel by Rigorous Sensor Model (RSM) approach. However, in the situation where the reliable orbital and sensor parameters are not available, the Rational Function Model (RFM) can be used as an alternative solution for the geometric correction of VHRS imagery. This paper discusses the VHRS utilization with a comprehensive approach that can be implemented in a local coordinate system i.e. the Indonesian Geospatial Reference System for the production of the reliable VHRS imageries.

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

Sign in / Sign up

Export Citation Format

Share Document