scholarly journals MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NEURAL NETWORK

2019 ◽  
Vol IV-5/W2 ◽  
pp. 111-117 ◽  
Author(s):  
S. Timilsina ◽  
S. K. Sharma ◽  
J. Aryal
Keyword(s):  
Neural Network ◽  
Management Practices ◽  
Urban Trees ◽  
Urban Tree ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Object Based ◽  
Training Samples ◽  
Level Statistics ◽  
Tree Coverage

Abstract. Urban trees offer significant benefits for improving the sustainability and liveability of cities, but its monitoring is a major challenge for urban planners. Remote-sensing based technologies can effectively detect, monitor and quantify urban tree coverage as an alternative to field-based measurements. Automatic extraction of urban land cover features with high accuracy is a challenging task and it demands artificial intelligence workflows for efficiency and thematic quality. In this context, the objective of this research is to map urban tree coverage per cadastral parcel of Sandy Bay, Hobart from very high-resolution aerial orthophoto and LiDAR data using an Object Based Convolution Neural Network (CNN) approach. Instead of manual preparation of a large number of required training samples, automatically classified Object based image analysis (OBIA) output is used as an input samples to train CNN method. Also, CNN output is further refined and segmented using OBIA to assess the accuracy. The result shows 93.2% overall accuracy for refined CNN classification. Similarly, the overlay of improved CNN output with cadastral parcel layer shows that 21.5% of the study area is covered by trees. This research demonstrates that the accuracy of image classification can be improved by using a combination of OBIA and CNN methods. Such a combined method can be used where manual preparation of training samples for CNN is not preferred. Also, our results indicate that the technique can be implemented to calculate parcel level statistics for urban tree coverage that provides meaningful metrics to guide urban planning and land management practices.

scholarly journals Mapping Urban Tree Cover Changes Using Object-Based Convolution Neural Network (OB-CNN)

2020 ◽  
Vol 12 (18) ◽  
pp. 3017
Author(s):  
Shirisa Timilsina ◽  
Jagannath Aryal ◽  
Jamie B. Kirkpatrick
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Urban Trees ◽  
Tree Cover ◽  
Urban Tree ◽  
Object Based ◽  
Parcel Size ◽  
Tree Coverage ◽  
The Relationship ◽  
Urban Tree Cover

Urban trees provide social, economic, environmental and ecosystem services benefits that improve the liveability of cities and contribute to individual and community wellbeing. There is thus a need for effective mapping, monitoring and maintenance of urban trees. Remote sensing technologies can effectively map and monitor urban tree coverage and changes over time as an efficient and low-cost alternative to field-based measurements, which are time consuming and costly. Automatic extraction of urban land cover features with high accuracy is a challenging task, and it demands object based artificial intelligence workflows for efficiency and thematic accuracy. The aim of this research is to effectively map urban tree cover changes and model the relationship of such changes with socioeconomic variables. The object-based convolutional neural network (CNN) method is illustrated by mapping urban tree cover changes between 2005 and 2015/16 using satellite, Google Earth imageries and Light Detection and Ranging (LiDAR) datasets. The training sample for CNN model was generated by Object Based Image Analysis (OBIA) using thresholds in a Canopy Height Model (CHM) and the Normalised Difference Vegetation Index (NDVI). The tree heatmap produced from the CNN model was further refined using OBIA. Tree cover loss, gain and persistence was extracted, and multiple regression analysis was applied to model the relationship with socioeconomic variables. The overall accuracy and kappa coefficient of tree cover extraction was 96% and 0.77 for 2005 images and 98% and 0.93 for 2015/16 images, indicating that the object-based CNN technique can be effectively implemented for urban tree coverage mapping and monitoring. There was a decline in tree coverage in all suburbs. Mean parcel size and median household income were significantly related to tree cover loss (R2 = 58.5%). Tree cover gain and persistence had positive relationship with tertiary education, parcel size and ownership change (gain: R2 = 67.8% and persistence: R2 = 75.3%). The research findings demonstrated that remote sensing data with intelligent processing can contribute to the development of policy input for management of tree coverage in cities.

scholarly journals Urban Tree Species Identification and Carbon Stock Mapping for Urban Green Planning and Management

Forests ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 1226
Author(s):  
Md Abdul Mueed Choudhury ◽  
Ernesto Marcheggiani ◽  
Francesca Despini ◽  
Sofia Costanzini ◽  
Paolo Rossi ◽  
...  
Keyword(s):  
Tree Species ◽  
Carbon Stock ◽  
Urban Trees ◽  
Species Classification ◽  
Urban Green ◽  
Urban Tree ◽  
Object Based Image Analysis ◽  
Planning And Management ◽  
Object Based ◽  
Atmospheric Carbon

Recently, the severe intensification of atmospheric carbon has highlighted the importance of urban tree contributions in atmospheric carbon mitigations in city areas considering sustainable urban green planning and management systems. Explicit and timely information on urban trees and their roles in the atmospheric Carbon Stock (CS) are essential for policymakers to take immediate actions to ameliorate the effects of deforestation and their worsening outcomes. In this study, a detailed methodology for urban tree CS calibration and mapping was developed for the small urban area of Sassuolo in Italy. For dominant tree species classification, a remote sensing approach was applied, utilizing a high-resolution WV3 image. Five dominant species were identified and classified by applying the Object-Based Image Analysis (OBIA) approach with an overall accuracy of 78%. The CS calibration was done by utilizing an allometric model based on the field data of tree dendrometry—i.e., Height (H) and Diameter at Breast Height (DBH). For geometric measurements, a terrestrial photogrammetric approach known as Structure-from-Motion (SfM) was utilized. Out of 22 randomly selected sample plots of 100 square meters (10 m × 10 m) each, seven plots were utilized to validate the results of the CS calibration and mapping. In this study, CS mapping was done in an efficient and convenient way, highlighting higher CS and lower CS zones while recognizing the dominant tree species contributions. This study will help city planners initiate CS mapping and predict the possible CS for larger urban regions to ensure a sustainable urban green management system.

scholarly journals Fast Urban Land Cover Mapping Exploiting Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 14 (1) ◽  
pp. 36
Author(s):  
Naomi Petrushevsky ◽  
Marco Manzoni ◽  
Andrea Monti-Guarnieri
Keyword(s):  
Land Cover ◽  
State Of The Art ◽  
Rapid Change ◽  
Object Based Image Analysis ◽  
Urban Land Cover ◽  
Monitoring Tools ◽  
Object Based ◽  
Urban Extent ◽  
Manual Classification ◽  
Sentinel 2

The rapid change and expansion of human settlements raise the need for precise remote-sensing monitoring tools. While some Land Cover (LC) maps are publicly available, the knowledge of the up-to-date urban extent for a specific instance in time is often missing. The lack of a relevant urban mask, especially in developing countries, increases the burden on Earth Observation (EO) data users or requires them to rely on time-consuming manual classification. This paper explores fast and effective exploitation of Sentinel-1 (S1) and Sentinel-2 (S2) data for the generation of urban LC, which can be frequently updated. The method is based on an Object-Based Image Analysis (OBIA), where one Multi-Spectral (MS) image is used to define clusters of similar pixels through super-pixel segmentation. A short stack (<2 months) of Synthetic Aperture Radar (SAR) data is then employed to classify the clusters, exploiting the unique characteristics of the radio backscatter from human-made targets. The repeated illumination and acquisition geometry allows defining robust features based on amplitude, coherence, and polarimetry. Data from ascending and descending orbits are combined to overcome distortions and decrease sensitivity to the orientation of structures. Finally, an unsupervised Machine Learning (ML) model is used to separate the signature of urban targets in a mixed environment. The method was validated in two sites in Portugal, with diverse types of LC and complex topography. Comparative analysis was performed with two state-of-the-art high-resolution solutions, which require long sensing periods, indicating significant agreement between the methods (averaged accuracy of around 90%).

scholarly journals Improved Classification of Urban Trees Using a Widespread Multi-Temporal Aerial Image Dataset

2020 ◽  
Vol 12 (15) ◽  
pp. 2475 ◽  
Author(s):  
Daniel S. W. Katz ◽  
Stuart A. Batterman ◽  
Shannon J. Brines
Keyword(s):  
Classification Accuracy ◽  
World View ◽  
Urban Trees ◽  
Aerial Images ◽  
Aerial Image ◽  
Urban Tree ◽  
Object Based ◽  
Street Tree ◽  
Multi Temporal

Urban tree identification is often limited by the accessibility of remote sensing imagery but has not yet been attempted with the multi-temporal commercial aerial photography that is now widely available. In this study, trees in Detroit, Michigan, USA are identified using eight high resolution red, green, and blue (RGB) aerial images from a commercial vendor and publicly available LiDAR data. Classifications based on these data were compared with classifications based on World View 2 satellite imagery, which is commonly used for this task but also more expensive. An object-based classification approach was used whereby tree canopies were segmented using LiDAR, and a street tree database was used for generating training and testing datasets. Overall accuracy using multi-temporal aerial images and LiDAR was 70%, which was higher than the accuracy achieved with World View 2 imagery and LiDAR (63%). When all data were used, classification accuracy increased to 74%. Taxa identified with high accuracy included Acer platanoides and Gleditsia, and taxa that were identified with good accuracy included Acer, Platanus, Quercus, and Tilia. Our results show that this large catalogue of multi-temporal aerial images can be leveraged for urban tree identification. While classification accuracy rates vary between taxa, the approach demonstrated can have practical value for socially or ecologically important taxa.

scholarly journals Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach

2020 ◽  
Vol 12 (3) ◽  
pp. 504 ◽  
Author(s):  
Tengfei Su ◽  
Shengwei Zhang ◽  
Tingxi Liu
Keyword(s):  
Remote Sensing ◽  
Active Learning ◽  
Classification Performance ◽  
Landscape Patterns ◽  
Training Set ◽  
Object Based Image Analysis ◽  
Classification Uncertainty ◽  
Object Based ◽  
Training Samples ◽  
Key Steps

In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.

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