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Comparing Human Versus Machine-Driven Cadastral Boundary Feature Extraction

REMOTE SENSING 2019年第14期11卷 1662-1662页

作者： Nyandwi, Emmanuel Koeva, Mila Kohli, Divyani Bennett, Rohan Univ Rwanda CST SABE Dept Geog & Urban Planning BP 3900 Kigali City Rwanda Univ Twente Fac Geoinformat Sci & Earth Observat ITC NL-7500 AE Enschede Netherlands Swinburne Business Sch Dept Business Technol & Entrepreneurship BA1231 Hawthorn campus Melbourne Vic 3122 Australia

The objective to fast-track the mapping and registration of large numbers of unrecorded land rights globally has led to the experimental application of Artificial Intelligence in the domain of land administration, and specifically the application of automated visual cognition techniques for cadastral mapping tasks. In this research, we applied and compared the ability of rule-based systems within object-based image analysis (OBIA), as opposed to human analysis, to extract visible cadastral boundaries from very high-resolution World View-2 images, in both rural and urban settings. From our experiments, machine-based techniques were able to automatically delineate a good proportion of rural parcels with explicit polygons where the correctness of the automatically extracted boundaries was 47.4% against 74.24% for humans and the completeness of 45% for the machine compared to 70.4% for humans. On the contrary, in the urban area, automatic results were counterintuitive: even though urban plots and buildings are clearly marked with visible features such as fences, roads and tacitly perceptible to eyes, automation resulted in geometrically and topologically poorly structured data. Thus, these could neither be geometrically compared with human digitisation, nor actual cadastral data from the field. The results of this study provide an updated snapshot with regards to the performance of contemporary machine-driven feature extraction techniques compared to conventional manual digitising. In our methodology, using an iterative approach of segmentation and classification, we demonstrated how to overcome the weaknesses of having undesirable segments due to intra-parcel and inter-parcel variability, when using segmentation approaches for cadastral feature delineation. We also demonstrated how we can easily implement a geometric comparison framework within the Esri's ArcGIS software environment and firmly believe the developed methodology can be reproduced.

关键词： cadastral intelligence manual digitisation expert parameterisation land administration land management automatic feature extraction object-based image analysis

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Vineyard classification using OBIA on UAV-based RGB and multispectral data: A case study in different wine regions

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2022年 196卷 1页

作者： Padua, Luis Matese, Alessando Di Gennaro, Salvatore Filippo Morais, Raul Peres, Emanuel Sousa, Joaquim J. Natl Res Council CNR Inst BioEcon IBE Via Caproni 8 I-50145 Florence Italy Univ Tros Os Montes & Alto Douro Ctr Res & Technol Agroenvironm & Biol Sci P-5000801 Vila Real Portugal Univ Tros Os Montes & Alto Douro Sch Sci & Technol P-5000801 Vila Real Portugal INESC Technol & Sci INESC TEC Ctr Robot Ind & Intelligent Syst CRIIS P-4200465 Porto Portugal

Vineyard classification is an important process within viticulture-related decision-support systems. Indeed, it improves grapevine vegetation detection, enabling both the assessment of vineyard vegetative properties and the optimization of in-field management tasks. Aerial data acquired by sensors coupled to unmanned aerial vehicles (UAVs) may be used to achieve it. Flight campaigns were conducted to acquire both RGB and multispectral data from three vineyards located in Portugal and in Italy. Red, green, blue and near infrared orthorectified mosaics resulted from the photogrammetric processing of the acquired data. They were then used to calculate RGB and multispectral vegetation indices, as well as a crop surface model (CSM). Three different supervised machine learning (ML) approaches-support vector machine (SVM), random forest (RF) and artificial neural network (ANN)-were trained to classify elements present within each vineyard into one of four classes: grapevine, shadow, soil and other vegetation. The trained models were then used to classify vineyards objects, generated from an object-based image analysis (OBIA) approach, into the four classes. Classification outcomes were compared with an automatic point-cloud classification approach and threshold-based approaches. Results shown that ANN provided a better overall classification performance, regardless of the type of features used. Features based on RGB data showed better performance than the ones based only on multispectral data. However, a higher performance was achieved when using features from both sensors. The methods presented in this study that resort to data acquired from different sensors are suitable to be used in the vineyard classification process. Furthermore, they also may be applied in other land use classification scenarios.

关键词： Precision viticulture Aerial imagery object-based image analysis Artificial neural network Random forest Support vector machine

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Conversion of Natural Wetland to Farmland in the Tumen River Basin: Human and Environmental Factors

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REMOTE SENSING 2021年第17期13卷 3498-3498页

作者： Liu, Yuyan Jin, Ri Zhu, Weihong Yanbian Univ Coll Geog & Ocean Sci Hunchun 133300 Peoples R China Jilin Prov Joint Key Lab Changbai Mt Wetland & Ec Changchun 130102 Peoples R China Jilin Prov Key Lab Changbai Mt Wetland Ecosyst Fu Hunchun 133300 Peoples R China Tumen River Basin Wetland & Ecol Int Joint Res Ct Hunchun 133300 Peoples R China Yanbian Univ Wetland Res Ctr Hunchun 133300 Peoples R China

Wetlands play an important role in the terrestrial ecosystem. However, agricultural activities have resulted in a significant decrease in natural wetlands around the world. In the Tumen River Basin (TRB), a border area between China, the Democratic People's Republic of Korea (DPRK), and Russia, natural wetlands have been reclaimed and converted into farmland, primarily due to the migration practices of Korean-Chinese. To understand the spatial and temporal patterns of this conversion from wetlands to farmland, Landsat remote sensing images from four time periods were analyzed. Almost 30 years of data were extracted using the object-oriented classification method combined with random forest classification. In addition, statistical analysis was conducted on the conversion from natural wetland to farmland and from farmland to wetland, as well as on the relationship between the driving factors. The results revealed that a loss of 49.2% (12,540.1 ha) of natural wetlands in the Chinese portion of the TRB was due to agricultural encroachment for grain production. At the sub-basin scale, the largest area of natural wetland converted into farmland in the past 30 years was in the Hunchun River Basin (HCH), which accounts for 22.0% (2761.2 ha) of the total. Meanwhile, 6571.4 ha of natural wetlands, mainly in the Gaya River Basin (GYH), have been restored from farmland. These changes are closely related to the migration of the agricultural populations.

关键词： natural wetland farmland Tumen river basin multi-scale analysis object-based image analysis

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High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite images

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REMOTE SENSING 2024年第2期16卷 282-282页

作者： Victor, Brandon Nibali, Aiden Newman, Saul Justin Coram, Tristan Pinto, Francisco Reynolds, Matthew Furbank, Robert T. He, Zhen La Trobe Univ Dept Math & Comp Sci Melbourne Vic 3086 Australia Univ Oxford Leverhulme Ctr Demog Sci Oxford OX1 1JD England Australian Grain Technol AGT Roseworthy SA 5371 Australia Int Maize & Wheat Improvement Ctr CIMMYT Texcoco 56237 MX Mexico Wageningen Univ Ctr Crop Syst Anal Dept Plant Sci NL-6700 AK Wageningen Netherlands Australian Natl Univ Res Sch Biol Div Plant Sci Acton ACT 2601 Australia

To ensure global food security, crop breeders conduct extensive trials across various locations to discover new crop varieties that grow more robustly, have higher yields, and are resilient to local stress factors. These trials consist of thousands of plots, each containing a unique crop variety monitored at intervals during the growing season, requiring considerable manual effort. In this study, we combined satellite imagery and deep learning techniques to automatically collect plot-level phenotypes from plant breeding trials in South Australia and Sonora, Mexico. We implemented two novel methods, utilising state-of-the-art computer vision architectures, to predict plot-level phenotypes: flowering, canopy cover, greenness, height, biomass, and normalised difference vegetation index (NDVI). The first approach uses a classification model to predict for just the centred plot. The second approach predicts per-pixel and then aggregates predictions to determine a value per-plot. Using a modified ResNet18 model to predict the centred plot was found to be the most effective method. These results highlight the exciting potential for improving crop trials with remote sensing and machine learning.

关键词： agriculture deep learning object-based image analysis optical imagery plant breeding

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Spatio-temporal landslide inventory and susceptibility assessment using Sentinel-2 in the Himalayan mountainous region of Pakistan

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2022年第11期194卷 845-845页

作者： Bacha, Alam Sher Shafique, Muhammad van der Werff, Harald van der Meijde, Mark Hussain, Mian Luqman Wahid, Sohail Univ Peshawar Natl Ctr Excellence Geol Peshawar Pakistan Inst Space Technol Natl Ctr GIS & Space Applicat Geosci GIS & Space Applicat Geosci GSAG Lab Islamabad Pakistan Univ Twente Fac Geoinformat Sci & Earth Observat ITC Enschede Netherlands

The 2005 Kashmir earthquake has triggered widespread landslides in the Himalayan mountains in northern Pakistan and surrounding areas, some of which are active and are still posing a significant risk. Landslides triggered by the 2005 Kashmir earthquake are extensively studied;nevertheless, spatio-temporal landslide susceptibility assessment is lacking. This can be partially attributed to the limited availability of high temporal resolution remote sensing data. We present a semi-automated technique to use the Sentinel-2 MSI data for co-seismic landslide detection, landslide activities monitoring, spatio-temporal change detection, and spatio-temporal susceptibility mapping. Time series Sentinel-2 MSI images for the period of 2016-2021 and ALOS PALSAR DEM are used for semi-automated landslide inventory map development and temporal change analysis. Spectral information combined with topographical, contextual, textural, and morphological characteristics of the landslide in Sentinel-2 images is applied for landslide detection. Subsequently, spatio-temporal landslide susceptibility maps are developed utilizing the weight of evidence statistical modeling with seven causative factors, i.e., elevation, slope, geology, aspect, distance to fault, distance to roads, and distance to streams. The results reveal that landslide occurrence increased from 2016 to 2021 and that the coverage of areas of relatively high susceptibility has increased in the study area.

关键词： Landslide Spatial and temporal changes Sentinel-2 MSI object-based image analysis Susceptibility mapping Himalaya

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Estimativa de biomassa e estoque de carbono de vegetação macrofítica a partir de dados espectrais e de campo, Banhado Grande, RS

Estimativa de biomassa e estoque de carbono de vegetação m...

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作者： Belloli, Tássia Fraga

Given the climate change scenario, the wetlands have stood out for acting substantially as atmospheric CO2 sinks from their fixation on plant biomass and peat bogs. However, the degradation of these ecosystems causes wetlands to move from CO2-fixing ecosystems to a source of greenhouse gases. Thus, quantifying and monitoring biomass is of great importance for preserving carbon stocks, and accurate carbon estimates can contribute to the preservation, loss prevention and restoration of these ecosystems. This study aimed to estimate plant biomass and organic carbon stocks of Scirpus giganteus species in Banhado Grande, from spectral data and field data. For this purpose, two methods were used: i) delimitation of the species area by Geographic object based image analysis (GEOBIA) and Ramdon Forest data mining, integrating Sentinel 1 and 2A sensor images; and ii) correlation and linear regression analysis from field and spectral data from the PlanetScope and Sentinel-2A sensors. Vegetation collections were performed in an experimental area in Banhado Grande, municipality of Glorinha-RS, during an annual cycle. In GEOBIA, the Random Forest (RF) data mining method was used to classify vegetation species in Banhado Grande, aiming to delimit the area occupied by the species *** and to estimate the stocks of biophysical variables for their total coverage area. The regression equations involved as dependent variables (y): oboveground biomass and organic carbon, obtained directly in the samples, and as independent variables (x) the spectral bands and the vegetation indices (VIs). The statistical treatment involved the analysis of the correlation matrix (r) between the variables x and y; the simple and multiple linear regression analysis, with the following statistics: R², R²aj., RMSE, CV%, SD and residual analysis. As a result, the GEOBIA classification reached an accuracy of 91.3% and 91% for the Emerging class, corresponding to the *** area (1,507 ha). Consider

关键词： Sensoriamento remoto Aboveground biomass blue carbon Áreas úmidas Biomassa remote sensing wetlands marshes Banhados object-based image analysis Carbono azul regression models Dissertação

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Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility analysis along the Karnali Highway, Nepal

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REMOTE SENSING 2019年第19期11卷 2284-2284页

作者： Amatya, Pukar Kirschbaum, Dalia Stanley, Thomas Univ Space Res Assoc Goddard Earth Sci Technol & Res Columbia MD 21046 USA NASA Hydrol Sci Lab Goddard Space Flight Ctr Greenbelt MD 20771 USA

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m(2) were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500-1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.

关键词： Karnali highway landslides very high-resolution optical imagery object-based image analysis DigitalGlobe PlanetScope landslide susceptibility logistic regression eCognition

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Identifying Soil Erosion Processes in Alpine Grasslands on Aerial imagery with a U-Net Convolutional Neural Network

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REMOTE SENSING 2020年第24期12卷 4149-4149页

作者： Samarin, Maxim Zweifel, Lauren Roth, Volker Alewell, Christine Univ Basel Dept Math & Comp Sci Spiegelgasse 1 CH-4051 Basel Switzerland Univ Basel Dept Environm Sci Bernoullistr 30 CH-4056 Basel Switzerland

Erosion in alpine grasslands is a major threat to ecosystem services of alpine soils. Natural causes for the occurrence of soil erosion are steep topography and prevailing climate conditions in combination with soil fragility. To increase our understanding of ongoing erosion processes and support sustainable land-use management, there is a need to acquire detailed information on spatial occurrence and temporal trends. Existing approaches to identify these trends are typically laborious, have lack of transferability to other regions, and are consequently only applicable to smaller regions. In order to overcome these limitations and create a sophisticated erosion monitoring tool capable of large-scale analysis, we developed a model based on U-Net, a fully convolutional neural network, to map different erosion processes on high-resolution aerial images (RGB, 0.25-0.5 m). U-Net was trained on a high-quality data set consisting of labeled erosion sites mapped with object-based image analysis (OBIA) for the Urseren Valley (Central Swiss Alps) for five aerial images (16 year period). We used the U-Net model to map the same study area and conduct quality assessments based on a held-out test region and a temporal transferability test on new images. Erosion classes are assigned according to their type (shallow landslide and sites with reduced vegetation affected by sheet erosion) or land-use impacts (livestock trails and larger management affected areas). We show that results obtained by OBIA and U-Net follow similar linear trends for the 16 year study period, exhibiting increases in total degraded area of 167% and 201%, respectively. Segmentations of eroded sites are generally in good agreement, but also display method-specific differences, which lead to an overall precision of 73%, a recall of 84%, and a F1-score of 78%. Our results show that U-Net is transferable to spatially (within our study area) and temporally unseen data (data from new years) and is therefore a method

关键词： deep learning semantic segmentation remote sensing object-based image analysis erosion mapping landslides livestock trails sheet erosion

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Gradient Boosting Machine and object-based CNN for Land Cover Classification

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REMOTE SENSING 2021年第14期13卷 2709-2709页

作者： Bui, Quang-Thanh Chou, Tien-Yin Hoang, Thanh-Van Fang, Yao-Min Mu, Ching-Yun Huang, Pi-Hui Pham, Vu-Dong Nguyen, Quoc-Huy Do Thi Ngoc Anh Pham, Van-Manh Meadows, Michael E. Vietnam Natl Univ VNU Univ Sci Fac Geog Ctr Appl Res Remote Sensing & GIS CARGIS 334 Nguyen Trai Hanoi 11416 Vietnam Feng Chia Univ Geog Informat Syst Res Ctr Taichung 40724 Taiwan VNU Univ Sci Fac Geog 334 Nguyen Trai Hanoi 11416 Vietnam Univ Capetown Dept Environm & Geog Sci ZA-7701 Rondebosh South Africa East China Normal Univ Sch Geog Sci Shanghai 200241 Peoples R China Zhejiang Normal Univ Coll Geog & Environm Sci Jinhua 321004 Zhejiang Peoples R China

In regular convolutional neural networks (CNN), fully-connected layers act as classifiers to estimate the probabilities for each instance in classification tasks. The accuracy of CNNs can be improved by replacing fully connected layers with gradient boosting algorithms. In this regard, this study investigates three robust classifiers, namely XGBoost, LightGBM, and Catboost, in combination with a CNN for a land cover study in Hanoi, Vietnam. The experiments were implemented using SPOT7 imagery through (1) image segmentation and extraction of features, including spectral information and spatial metrics, (2) normalization of attribute values and generation of graphs, and (3) using graphs as the input dataset to the investigated models for classifying six land cover classes, namely House, Bare land, Vegetation, Water, Impervious Surface, and Shadow. The results show that CNN-based XGBoost (Overall accuracy = 0.8905), LightGBM (0.8956), and CatBoost (0.8956) outperform the other methods used for comparison. It can be seen that the combination of object-based image analysis and CNN-based gradient boosting algorithms significantly improves classification accuracies and can be considered as alternative methods for land cover analysis.

关键词： object-based image analysis gradient boosting convolutional neural network land cover

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A Combination of OBIA and Random Forest based on Visible UAV Remote Sensing for Accurately Extracted Information about Weeds in Areas with Different Weed Densities in Farmland

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REMOTE SENSING 2023年第19期15卷 4696页

作者： Feng, Chao Zhang, Wenjiang Deng, Hui Dong, Lei Zhang, Houxi Tang, Ling Zheng, Yu Zhao, Zihan Chengdu Univ Technol Coll Earth Sci Chengdu 610059 Peoples R China Tibet Autonomous Reg Geol Min Explorat & Dev Bur 2 Geol Team Lhasa 850007 Peoples R China Fujian Agr & Forestry Univ Coll Forestry Fuzhou 350002 Peoples R China

Weeds have a significant impact on the growth of rice. Accurate information about weed infestations can provide farmers with important information to facilitate the precise use of chemicals. In this study, we utilized visible light images captured by UAVs to extract information about weeds in areas of two densities on farmland. First, the UAV images were segmented using an optimal segmentation scale, and the spectral, texture, index, and geometric features of each segmented object were extracted. Cross-validation and recursive feature elimination techniques were combined to reduce the dimensionality of all features to obtain a better feature set. Finally, we analyzed the extraction effect of different feature dimensions based on the random forest (RF) algorithm to determine the best feature dimensions, and then we further analyzed the classification result of machine learning algorithms, such as random forest, support vector machine (SVM), decision tree (DT), and K-nearest neighbors (KNN) and compared them based on the best feature dimensions. Using the extraction results of the best classifier, we created a zoning map of the weed infestations in the study area. The results indicated that the best feature subset achieved the highest accuracy, with respective overall accuracies of 95.38% and 91.33% for areas with dense and sparse weed densities, respectively, and F1-scores of 94.20% and 90.57. Random forest provided the best extraction results for each machine learning algorithm in the two experimental areas. When compared to the other algorithms, it improved the overall accuracy by 1.74-12.14% and 7.51-11.56% for areas with dense and sparse weed densities, respectively. The F1-score improved by 1.89-17.40% and 7.85-10.80%. Therefore, the combination of object-based image analysis (OBIA) and random forest based on UAV remote sensing accurately extracted information about weeds in areas with different weed densities for farmland, providing effective information suppor

关键词： UAV remote sensing weeds random forest object-based image analysis

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