Mining activities are the leading cause of deforestation, land-use changes, and pollution. Land use/cover mapping in Vietnam every five years is not useful to monitor land covers in mining areas, especially in the Central Highland region. It is necessary to equip managers with a better tool to monitor and map land cover using high-resolution images. Therefore, the authors proposed using the U-Net convolutional network for land-cover classification based on multispectral Unmanned aerial vehicle (UAV) image in a mining area of Daknong province, Vietnam. An area of 0.5kmx0.8km was used for training and testing seven U-Net models using seven optimizer function types. The final U-Net model can interpret six land cover types: (1) open-case mining lands, (2) old permanent croplands, (3) young permanent croplands, (4) grasslands, (5) bare soils, (6) water bodies. As a result, two models using Nadam and Adadelta optimizer function can be used to classify six land cover types with accuracy higher than 83%, especially in open-case mining lands and polluted streams flowed out from the mining areas. The trained U-Net models can potentially update new land cover types in other mining areas towards monitoring land cover changes in real-time in the future.

The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted from the natural wetland to the human-made wetland. It is anticipated that the conversion rate continues to increase due to economic development and urbanization. Therefore, monitoring and assessment of the wetland are essential for the coastal vulnerability assessment and geo-ecosystem management. The aim of this study is to propose and verify a new deep learning approach to interpret 9 of 19 coastal wetland types classified in the RAMSAR and MONRE systems for the Tien Yen estuary of Vietnam. Herein, a Resnet framework was integrated into the U-Net to optimize the performance of the proposed deep learning model. The Sentinel-2, ALOS-DEM, and NOAA-DEM satellite images were used as the input data, whereas the output is the predefined nine wetland types. As a result, two ResU-Net models using Adam and RMSprop optimizer functions show the accuracy higher than 85%, especially in forested intertidal wetlands, aquaculture ponds, and farm ponds. The better performance of these models was proved, compared to Random Forest and Support Vector Machine methods. After optimizing the ResU-Net models, they were also used to map the coastal wetland areas correctly in the northeastern part of Vietnam. The final model can potentially update new wetland types in the southern parts and islands in Vietnam towards wetland change monitoring in real time.

The shipping industry is pivotal in transporting approximately 90% of the world’s goods, and it is characterized by evolving trends in vessel sizes and energy-efficient designs. Continuous advancements in technology for ship management have focused on detecting and analyzing anomalous and illicit vessels. In this study, we introduce ShipGeoNet, a model designed to extract geometric features from ships captured in Sentinel-1 synthetic aperture radar (SAR) images. ShipGeoNet employs a combination of convolutional neural networks (CNNs) and nonlinear regression techniques to extract various geometric features of ships from SAR imagery. The model follows a two-step approach. First, it utilizes a modified Mask R-CNN architecture and the ViTDet model to accurately detect ships, generating high-quality object masks for precise localization. In the subsequent step, a regression model utilizes the detected ship masks to extract key geometric attributes, including length, width, and orientation. The proposed nonlinear regression techniques are specifically crafted to address the complex nonlinear deformations inherent in SAR images. Through extensive experiments on a large-scale SAR dataset, ShipGeoNet demonstrates its efficiency and accuracy in ship size extraction and matching, outperforming existing methods. Developing the ShipGeoNet model opens up possibilities for future applications in maritime surveillance, navigation, and environmental monitoring.