Paddy ground truth data collection and evaluation for land cover mapping

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

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A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

Journal of Applied and Natural Science ◽

10.31018/jans.v12i1.2209 ◽

2020 ◽

Vol 12 (1) ◽

pp. 9-12

Author(s):

Arjun G. Koppad ◽

Syeda Sarfin ◽

Anup Kumar Das

Keyword(s):

Land Use ◽

Land Cover ◽

Accuracy Assessment ◽

Ground Truth ◽

Land Cover Classification ◽

Data Sets ◽

Radiometric Calibration ◽

Ground Truth Data ◽

Dense Forest ◽

Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha). Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76. Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

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Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection

Canadian Journal of Civil Engineering ◽

10.1139/cjce-2019-0087 ◽

2020 ◽

Vol 47 (8) ◽

pp. 982-997

Author(s):

Mohamed H. Zaki ◽

Tarek Sayed ◽

Moataz Billeh

Keyword(s):

Data Collection ◽

Video Analysis ◽

Three Dimensional ◽

Ground Truth ◽

Traffic Data ◽

Ground Truth Data ◽

The Road ◽

Traffic Data Collection ◽

Bounding Boxes ◽

Automated Video Analysis

Video-based traffic analysis is a leading technology for streamlining transportation data collection. With traffic records from video cameras, unsupervised automated video analysis can detect various vehicle measures such as vehicle spatial coordinates and subsequently lane positions, speed, and other dynamic measures without the need of any physical interconnections to the road infrastructure. This paper contributes to the unsupervised automated video analysis by addressing two main shortcomings of the approach. The first objective is to alleviate tracking problems of over-segmentation and over-grouping by integrating region-based detection with feature-based tracking. This information, when combined with spatiotemporal constraints of grouping, can reduce the effects of these problems. This fusion approach offers a superior decision procedure for grouping objects and discriminating between trajectories of objects. The second objective is to model three-dimensional bounding boxes for the vehicles, leading to a better estimate of their geometry and consequently accurate measures of their position and travel information. This improvement leads to more precise measurement of traffic parameters such as average speed, gap time, and headway. The paper describes the various steps of the proposed improvements. It evaluates the effectiveness of the refinement process on data collected from traffic cameras in three different locations in Canada and validates the results with ground truth data. It illustrates the effectiveness of the improved unsupervised automated video analysis with a case study on 10 h of traffic data collection such as volume and headway measurements.

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Adaptive Feature Spaces for Land Cover Classification with Limited Ground Truth Data

Multiple Classifier Systems - Lecture Notes in Computer Science ◽

10.1007/3-540-45428-4_19 ◽

2002 ◽

pp. 189-200 ◽

Cited By ~ 5

Author(s):

Joseph T. Morgan ◽

Alex Henneguelle ◽

Melba M. Crawford ◽

Joydeep Ghosh ◽

Amy Neuenschwander

Keyword(s):

Land Cover ◽

Ground Truth ◽

Land Cover Classification ◽

Ground Truth Data ◽

Feature Spaces

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POI Mining for Land Use Classification: A Case Study

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090493 ◽

2020 ◽

Vol 9 (9) ◽

pp. 493 ◽

Cited By ~ 1

Author(s):

Renato Andrade ◽

Ana Alves ◽

Carlos Bento

Keyword(s):

Land Use ◽

Land Cover ◽

Urban Areas ◽

Smart Cities ◽

Ground Truth ◽

Land Use Classification ◽

Data Types ◽

Ground Truth Data ◽

Functional Roles ◽

Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

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EXTRACTING URBAN MORPHOLOGY FOR ATMOSPHERIC MODELING FROM MULTISPECTRAL AND SAR SATELLITE IMAGERY

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-w1-425-2017 ◽

2017 ◽

Vol XLII-1/W1 ◽

pp. 425-431 ◽

Cited By ~ 1

Author(s):

S. Wittke ◽

K. Karila ◽

E. Puttonen ◽

A. Hellsten ◽

M. Auvinen ◽

...

Keyword(s):

Land Cover ◽

Urban Canopy ◽

Ground Truth ◽

Bare Soil ◽

Urban Morphology ◽

Atmospheric Modeling ◽

Airborne Lidar ◽

Atmospheric Pollutant ◽

Ground Truth Data ◽

Land Cover Map

This paper presents an approach designed to derive an urban morphology map from satellite data while aiming to minimize the cost of data and user interference. The approach will help to provide updates to the current morphological databases around the world. The proposed urban morphology maps consist of two layers: 1) Digital Elevation Model (DEM) and 2) land cover map. Sentinel-2 data was used to create a land cover map, which was realized through image classification using optical range indices calculated from image data. For the purpose of atmospheric modeling, the most important classes are water and vegetation areas. The rest of the area includes bare soil and built-up areas among others, and they were merged into one class in the end. The classification result was validated with ground truth data collected both from field measurements and aerial imagery. The overall classification accuracy for the three classes is 91&thinsp;%. TanDEM-X data was processed into two DEMs with different grid sizes using interferometric SAR processing. The resulting DEM has a RMSE of 3.2 meters compared to a high resolution DEM, which was estimated through 20 control points in flat areas. Comparing the derived DEM with the ground truth DEM from airborne LIDAR data, it can be seen that the street canyons, that are of high importance for urban atmospheric modeling are not detectable in the TanDEM-X DEM. However, the derived DEM is suitable for a class of urban atmospheric models. Based on the numerical modeling needs for regional atmospheric pollutant dispersion studies, the generated files enable the extraction of relevant parametrizations, such as Urban Canopy Parameters (UCP).

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ASSESSMENT OF LAND USE AND LAND COVER CHANGE DETECTION BY USING REMOTE SENSING AND GIS TECHNIQUES IN THE COASTAL DESERTS, SOUTH OF IRAN

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-489-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 489-492

Author(s):

S. A. R. Hosseini ◽

H. Gholami ◽

Y. Esmaeilpoor

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Change Detection ◽

Urban Areas ◽

Ground Truth ◽

Landsat Images ◽

Ground Truth Data ◽

Sensing Technology ◽

Time Period

Abstract. Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Due to the large expanse of land change detection by the traditional methods is not sufficient and efficient; therefore, using of new methods such as remote sensing technology is necessary and vital This study evaluates LULC change in chabahar and konarak Coastal deserts, located in south of sistan and baluchestan province from 1988 to 2018 using Landsat images. Maximum likelihood classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. Then, taking ground truth data, the classified maps accuracy were assessed by calculating the Kappa coefficient and overall accuracy. The results for the time period of 1988–2018 are presented. Based on the results of the 30-year time period, vegetation has been decreased in area while urban areas have been developed. The area of saline and sandy lands has also increased.

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