A refined classification approach by integrating Landsat Operational Land Imager (OLI) and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area

Producing accurate land-use and land-cover (LULC) mapping is a long-standing challenge using solely optical remote-sensing data, especially in tropical regions due to the presence of clouds. To supplement this, RADARSAT images can be useful in assisting LULC mapping. The fusion of optical and active remote-sensing data is important for accurate LULC mapping because the data from different parts of the spectrum provide complementary information and often lead to increased classification accuracy. Also, the timeliness of using synthetic aperture radar (SAR) fills information gaps during overcast or hazy periods. Therefore, this research designed a refined classification procedure for LULC mapping for tropical regions. Determining the best method for mapping with a specific data source and study area is a major challenge because of the wide range of classification algorithms and methodologies available. In this study, different combinations and the potential of Landsat Operational Land Imager (OLI) and RADARSAT-2 SAR data were evaluated to select the best procedure for LULC classification. Results showed that the best filter for SAR speckle reduction is the 5 × 5 enhanced Lee. Furthermore, image-sharpening algorithms were employed to fuse Landsat multispectral and panchromatic bands and subsequently these algorithms were analysed in detail. The findings also confirmed that Gram–Schmidt (GS) performed better than the other techniques employed. Fused Landsat data and SAR images were then integrated to produce the LULC map. Different classification algorithms were adopted to classify the integrated Landsat and SAR data, and the maximum likelihood classifier (MLC) was considered the best approach. Finally, a suitable classification procedure was designed and proposed for LULC as mapping in tropical regions based on the results obtained. An overall accuracy of 98.62% was achieved from the proposed methodology. The proposed methodology is a useful tool in industry for mapping purposes. Additionally, it is also useful for researchers, who could extend the method for different data sources and regions

Assessment of the effects of expressway geometric design features on the frequency of accident crash rates using high-resolution laser scanning data and GIS

Accurate information on accidents and on the relevant factors that affect them is critical for establishing the relationship between accident frequency and explanatory factors. In this study, we present a simplified method to extract road geometric features accurately from very high-resolution laser scanning data to analyze accident frequency on the North-South Expressway in Malaysia. Using expressway geometric features (i.e. horizontal and vertical alignments) extracted from laser scanning data and accident histories, this research first developed an APM based on geometric regression and a geographic information system (GIS). Then, an elasticity analysis was conducted to investigate the relationship between accident occurrence and road geometric design features. Results of the case study showed that the length of the road segments (mean = 0.014, elasticity = 0.122), the number of vertical curves in a road section (mean = 4.797, elasticity = 0.999), and the presence of a horizontal curve in a road segment (mean = 2.746, elasticity = 0.877), the average distance to the nearest access point (mean = -0.001, elasticity = −0.035), and AADT (mean = 3.01, elasticity = 0.881) determined accident occurrence, all at a significance level of 5%. This study shows that laser scanning systems can provide an easy and efficient method to collect transportation data, particularly those for accident analysis

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks

Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two, 0.924 top-three, 0.729 F1-score, 0.676 recall and 0.790 precision for the second test dataset. The results demonstrated that the proposed ZSL is a promising tool for land cover mapping based on high-resolution photos

Mapping rubber trees based on phenological analysis of Landsat time series data-sets

This study proposes a strategy for accurate mapping of rubber trees through the analysis of Landsat time series datasets. The phenological dynamics of rubber trees were derived from the Normalized Difference Vegetation Index (NDVI) to verify the three important phenological metrics of rubber trees; defoliation, foliation and their growing stages. A decade (2006–2015) ago, Landsat time series NDVIs were used to study the strength of relationship between rubber trees, evergreen trees and oil palm trees. Two important results that could discriminate these three types of vegetation were found; firstly, a weak relationship of NDVIs between rubber trees and evergreen trees during the defoliation period (r2 = 0.1358) and secondly between rubber trees and oil palm trees during the growing period (r2 = 0.2029). This analysis was verified using Support Vector Machine to map the distribution of the three types of vegetation. An accurate mapping strategy of rubber trees was successfully formulated

A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings

A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides

Optimisation plays a key role in the application of machine learning in the spatial prediction of landslides. The common practice in optimising landslide prediction models is to search for optimal/suboptimal hyperparameter values in a number of predetermined hyperparameter configurations based on an objective function, i.e., k-fold cross-validation accuracy. However, the overhead of hyperparameter optimisation can be prohibitive, especially for computationally expensive algorithms. This paper introduces an optimisation approach based on meta-learning for the spatial prediction of landslides. The proposed approach is tested in a dense tropical forested area of Cameron Highlands, Malaysia. Instead of optimising prediction models with a large number of hyperparameter configurations, the proposed approach begins with promising configurations based on several basic and statistical meta-features. The proposed meta-learning approach was tested based on Bayesian optimisation as a hyperparameter tuning algorithm and random forest (RF) as a prediction model. The spatial database was established with a total of 63 historical landslides and 15 conditioning factors. Three RF models were constructed based on (1) default parameters as suggested by the sklearn library, (2) parameters suggested by the Bayesian optimisation (BO), and (3) parameters suggested by the proposed meta-learning approach (BO-ML). Based on five-fold cross-validation accuracy, the Bayesian method achieved the best performance for both the training (0.810) and test (0.802) datasets. The meta-learning approach achieved slightly lower accuracies than the Bayesian method for the training (0.769) and test (0.800) datasets. Similarly, based on F1-score and area under the receiving operating characteristic curves (AUROC), the models with optimised parameters either by the Bayesian or meta-learning methods produced more accurate landslide susceptibility assessment than the model with the default parameters. In the present approach, instead of learning from scratch, the meta-learning would begin with hyperparameter configurations optimal for the most similar previous datasets, which can be considerably helpful and time-saving for landslide modelings