In recent years, remote sensing researchers have investigated the use of different modalities (or combinations of modalities) for classification tasks. Such modalities can be extracted via a diverse range of sensors and images. Currently, there are no (or only a few) studies that have been done to increase the land cover classification accuracy via unmanned aerial vehicle (UAV)–digital surface model (DSM) fused datasets. Therefore, this study looks at improving the accuracy of these datasets by exploiting convolutional neural networks (CNNs). In this work, we focus on the fusion of DSM and UAV images for land use/land cover mapping via classification into seven classes: bare land, buildings, dense vegetation/trees, grassland, paved roads, shadows, and water bodies. Specifically, we investigated the effectiveness of the two datasets with the aim of inspecting whether the fused DSM yields remarkable outcomes for land cover classification. The datasets were: (i) only orthomosaic image data (Red, Green and Blue channel data), and (ii) a fusion of the orthomosaic image and DSM data, where the final classification was performed using a CNN. CNN, as a classification method, is promising due to hierarchical learning structure, regulating and weight sharing with respect to training data, generalization, optimization and parameters reduction, automatic feature extraction and robust discrimination ability with high performance. The experimental results show that a CNN trained on the fused dataset obtains better results with Kappa index of ~0.98, an average accuracy of 0.97 and final overall accuracy of 0.98. Comparing accuracies between the CNN with DSM result and the CNN without DSM result for the overall accuracy, average accuracy and Kappa index revealed an improvement of 1.2%, 1.8% and 1.5%, respectively. Accordingly, adding the heights of features such as buildings and trees improved the differentiation between vegetation specifically where plants were dense.

This paper proposes a UAV-based PM_2.5 air quality and temperature-humidity monitoring system. The system includes an air quality detector comprising four Arduino sensor modules. Specifically, it includes a dust (DSM501A) sensor and a temperature and humidity (DHT11) sensor. The NEO-6M GPS module and DS3231 real-time module are also included for input visualization. A DIY SD card logging shield and memory module is also available for data recording purposes. The Arduino-based board houses multiple sensors and all are programmable using the Arduino integrated development environment (IDE) coding tool. Measurements conducted in a vertical flight path show promise where comparisons with ground truth references data showed good similarity. Overall, the results point to the idea that a light-weight and portable system can be used for accurate and reliable remote sensing data collection (in this case, PM_2.5 concentration data and environmental data).

Since at least half of the world’s population resides and works within coastal land, the coastal zone processes and resource management is of great economic and social importance. One of the fundamental issues for coastal city planners, researchers, managers, and engineers is the coastal city land-use suitability. Land-use suitability is the ability of a given type of land to support a defined use. Rapid urbanization and consequent haphazard growth of cities result in deterioration of infrastructure facilities, loss of agricultural land, water bodies, open spaces, and many micro-climatic changes. Hence, accurate data on coastal city hazards are essential and valuable tools for coastal planning and management, sustainable coastal development, coastal environment conservation, selection of a site for coastal city structures, and coastal resources. In this investigation, the Delphi and Analytical Hierarchy Process (D-AHP) Hybrid model and Geographic Information System (GIS) technique for Coastal Land-Use Assessment (CLUA) are mapped to detect the most suitable and unsuitable areas in the Kuala Terengganu coastal zone. Furthermore, this research offered information not only on the present urban land-use trend and established amenity status in Kuala Terengganu, but also on the suitability of land for the potential establishment of urban facilities for improved urban planning and appropriate decision-making. Using the D-AHP Hybrid model and GIS tool for coastal city management is broadly practical for government, policymakers, and planners to appropriately strategize and plan for the future of coastal cities in Malaysia and other analog coastal cities around the world.

Image registration has been long used as a basis for the detection of moving objects. Registration techniques attempt to discover correspondences between consecutive frame pairs based on image appearances under rigid and affine transformations. However, spatial information is often ignored, and different motions from multiple moving objects cannot be efficiently modeled. Moreover, image registration is not well suited to handle occlusion that can result in potential object misses. This paper proposes a novel approach to address these problems. First, segmented video frames from unmanned aerial vehicle captured video sequences are represented using region adjacency graphs of visual appearance and geometric properties. Correspondence matching (for visible and occluded regions) is then performed between graph sequences by using multigraph matching. After matching, region labeling is achieved by a proposed graph coloring algorithm which assigns a background or foreground label to the respective region. The intuition of the algorithm is that background scene and foreground moving objects exhibit different motion characteristics in a sequence, and hence, their spatial distances are expected to be varying with time. Experiments conducted on several DARPA VIVID video sequences as well as self-captured videos show that the proposed method is robust to unknown transformations, with significant improvements in overall precision and recall compared to existing works.

Early detection of Basal Stem Rot (BSR) disease in oil palms is an important plantation management activity in Southeast Asia. Practical approaches for the best strategic approach toward the treatment of this disease that originated from Ganoderma Boninense require information about the status of infection. In spite of the availability of conventional methods to detect this disease, they are difficult to be used in plantation areas that are commonly large in terms of planting hectarage; therefore, there is an interest for a quick and delicate technique to facilitate the detection and monitoring of Ganoderma in its early stage. The main goal of this paper is to evaluate the use of remote sensing technique for the rapid detection of Ganoderma-infected oil palms using Unmanned Aerial Vehicle (UAV) imagery integrated with an Artificial Neural Network (ANN) model. Principally, we sought for the most representative mean and standard deviation values from green, red, and near-infrared bands, as well as the best palm circle radius, threshold limit, and the number of hidden neurons for different Ganoderma severity levels. With the obtained modified infrared UAV images at 0.026 m spatial resolution, early BSR infected oil palms were most satisfactorily detected with mean and standard deviation derived from a circle radius of 35 pixels of band green and near-infrared, 1/8 threshold limit, and ANN network by 219 hidden neurons, where the total classification accuracies achieved for training and testing the dataset were 97.52% and 72.73%, respectively. The results from this study signified the utilization of an affordable digital camera and UAV platforms in oil palm plantation, predominantly in disease management. The UAV images integrated with the Levenberg–Marquardt training algorithm illustrated its great potential as an aerial surveillance tool to detect early Ganoderma-infected oil palms in vast plantation areas in a rapid and inexpensive manner.

Considering the high-level details in an ultrahigh-spatial-resolution (UHSR) unmanned aerial vehicle (UAV) dataset, detailed mapping of heterogeneous urban landscapes is extremely challenging because of the spectral similarity between classes. In this study, adaptive hierarchical image segmentation optimization, multilevel feature selection, and multiscale (MS) supervised machine learning (ML) models were integrated to accurately generate detailed maps for heterogeneous urban areas from the fusion of the UHSR orthomosaic and digital surface model (DSM). The integrated approach commenced through a preliminary MS image segmentation parameter selection, followed by the application of three supervised ML models, namely, random forest (RF), support vector machine (SVM), and decision tree (DT). These models were implemented at the optimal MS levels to identify preliminary information, such as the optimal segmentation level(s) and relevant features, for extracting 12 land use/land cover (LULC) urban classes from the fused datasets. Using the information obtained from the first phase of the analysis, detailed MS classification was iteratively conducted to improve the classification accuracy and derive the final urban LULC maps. Two UAV-based datasets were used to develop and assess the effectiveness of the proposed framework. The hierarchical classification of the pilot study area showed that the RF was superior with an overall accuracy (OA) of 94.40% and a kappa coefficient (K) of 0.938, followed by SVM (OA = 92.50% and K = 0.917) and DT (OA = 91.60% and K = 0.908). The classification results of the second dataset revealed that SVM was superior with an OA of 94.45% and K of 0.938, followed by RF (OA = 92.46% and K = 0.916) and DT (OA = 90.46% and K = 0.893). The proposed framework exhibited an excellent potential for the detailed mapping of heterogeneous urban landscapes from the fusion of UHSR orthophoto and DSM images using various ML models.