Mapping Urban Trees Within Cadastral Parcels Using an Object-Based Convolutional Neural Network

Timilsina, Shirisa; Sharma, Saroj Kumar; Aryal, Jagannath

doi:10.5194/isprs-annals-iv-5-w2-111-2019

Cited by 23 publications

(21 citation statements)

References 18 publications

(21 reference statements)

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“…Some parts of this section are repeated from an earlier paper by the two senior authors [57]. The CNN workflow of Trimble's eCognition software Developer 9.4 was applied for tree extraction ( Figure 7).…”

Section: Object-based Cnn For Tree Cover Identificationmentioning

confidence: 99%

“…The hidden units are like neurons that are fully connected with each individual neuron from a previous layer [49,51]. CNN has proven successful in vegetation contexts [52][53][54][55][56][57]. Li et al [52] used the CNN algorithm in very high-resolution quick bird images for oil palm trees detection in Malaysia and achieved 87.95% overall accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Csillik et al [56] used the CNN workflow, with post-processing using GEOBIA, for identifying citrus trees in a complex agricultural area of California from unmanned aerial vehicle (UAV) imagery, achieving 96.24% overall accuracy. Timilsina et al [57] demonstrated that the accuracy of the image classifications can be improved by using a combination of OBIA and CNN methods to map the urban tree cover. No study has been published that maps temporal and spatial changes of tree covers using GEOBIA and CNN.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Section: Object-based Cnn For Tree Cover Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2020

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“…There were three steps: (1) to generate sample patches of lodging and non-lodging classes, (2) to create and train the model, and (3) to test the model and report its performance [43,44]. Some studies have been reported that use the CNN algorithm in this software for trees identification and classification [45,46] and dwelling identification [47,48]. In this study, a customized architecture for the CNN was used, which included three hidden layers and one fully connected layer (Figure 2), and it was applied to the three models.…”

Section: Cnn Architecture and Experimental Designmentioning

confidence: 99%

Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management

Zhao

Baenziger

et al. 2020

Agronomy

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Latest advances in unmanned aerial vehicle (UAV) technology and convolutional neural networks (CNNs) allow us to detect crop lodging in a more precise and accurate way. However, the performance and generalization of a model capable of detecting lodging when the plants may show different spectral and morphological signatures have not been investigated much. This study investigated and compared the performance of models trained using aerial imagery collected at two growth stages of winter wheat with different canopy phenotypes. Specifically, three CNN-based models were trained with aerial imagery collected at early grain filling stage only, at physiological maturity only, and at both stages. Results show that the multi-stage model trained by images from both growth stages outperformed the models trained by images from individual growth stages on all testing data. The mean accuracy of the multi-stage model was 89.23% for both growth stages, while the mean of the other two models were 52.32% and 84.9%, respectively. This study demonstrates the importance of diversity of training data in big data analytics, and the feasibility of developing a universal decision support system for wheat lodging detection and mapping multi-growth stages with high-resolution remote sensing imagery.

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“…CNN, a deep learning supervised neural network which uses labeled data, has been recognized as one of the most successful and widely used deep learning approaches [8,9]. For example, CNN has been used for object detection of trees [10][11][12][13][14][15], buildings extraction [16,17], ship detection [18,19], etc.…”

Section: Introductionmentioning

confidence: 99%

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

Kaplan

2021

Buildings

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Effective post-event emergency management contributes substantially to communities’ earthquake resilience, and one of the most crucial actions following an earthquake is building damage assessment. On-site inspections are dangerous, expensive, and time-consuming. Remote sensing techniques have shown great potential in localizing the most damaged regions and thus guiding aid and rescue operations in recent earthquakes. Furthermore, to prevent post-earthquake casualties, heavily damaged, unsafe buildings must be identified immediately since in most earthquakes, strong aftershocks can cause such buildings to collapse. The potential of the response spectrum concept for being associated with satellite-based remote sensing data for post-earthquake structural damage estimation was investigated in this study. In this respect, a response spectra-based post-earthquake structural damage estimation method aided by satellite-based remote sensing data was proposed to classify the buildings after an earthquake by prioritizing them based on their expected damage levels, in order to speed up the damage assessment process of critical buildings that can cause casualties in a possible strong aftershock. A case study application was implemented in the Bayrakli region in Izmir, Turkey, the most affected area by the Samos earthquake, on 30 October 2020. The damage estimations made in this research were compared with the in situ damage assessment reports prepared by the Republic of Turkey Ministry of Environment and Urbanization experts. According to the accuracy assessment results, the sensitivity of the method is high (91%), and the necessary time spent by the in situ damage assessment teams to detect the critical buildings would have been significantly reduced for the study area.

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Mapping Urban Trees Within Cadastral Parcels Using an Object-Based Convolutional Neural Network

Cited by 23 publications

References 18 publications

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

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

Automatic Wheat Lodging Detection and Mapping in Aerial Imagery to Support High-Throughput Phenotyping and In-Season Crop Management

Response Spectra-Based Post-Earthquake Rapid Structural Damage Estimation Approach Aided with Remote Sensing Data: 2020 Samos Earthquake

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