Human-induced deforestation has a major impact on forest ecosystems and therefore its detection and analysis methods should be improved. This study classified landscape affected by human-induced deforestation efficiently using high-resolution remote sensing and deep-learning. The SegNet and U-Net algorithms were selected for application with high-resolution remote sensing data obtained by the Kompsat-3 satellite. Land and forest cover maps were used as base data to construct accurate deep-learning datasets of deforested areas at high spatial resolution, and digital maps and a softwood database were used as reference data. Sites were classified into forest and non-forest areas, and a total of 13 areas (2 forest and 11 non-forest) were selected for analysis. Overall, U-Net was more accurate than SegNet (74.8% vs. 63.3%). The U-Net algorithm was about 11.5% more accurate than the SegNet algorithm, although SegNet performed better for the hardwood and bare land classes. The SegNet algorithm misclassified many forest areas, but no non-forest area. There was reduced accuracy of the U-Net algorithm due to misclassification among sub-items, but U-Net performed very well at the forest/non-forest area classification level, with 98.4% accuracy for forest areas and 88.5% for non-forest areas. Thus, deep-learning modeling has great potential for estimating human-induced deforestation in mountain areas. The findings of this study will contribute to more efficient monitoring of damaged mountain forests and the determination of policy priorities for mountain area restoration.

In this study, we classified land cover using SegNet, a deep-learning model, and we assessed its classification accuracy in comparison with the support-vector-machine (SVM) and random-forest (RF) machine-learning models. The land-cover classification was based on aerial orthoimagery with a spatial resolution of 1 m for the input dataset, and Level-3 land-use and land-cover (LULC) maps with a spatial resolution of 1 m as the reference dataset. The study areas were the Namhan and Bukhan River Basins, where significant urbanization occurred between 2010 and 2012. The hyperparameters were selected by comparing the validation accuracy of the models based on the parameter changes, and they were then used to classify four LU types (urban, crops, forests, and water). The results indicated that SegNet had the highest accuracy (91.54%), followed by the RF (52.96%) and SVM (50.27%) algorithms. Both machine-learning models showed lower accuracy than SegNet in classifying all land-cover types, except forests, with an overall-accuracy (OA) improvement of approximately 40% for SegNet. Next, we applied SegNet to detect land-cover changes according to aerial orthoimagery of Namyangju city, obtained in 2010 and 2012; the resulting OA values were 86.42% and 78.09%, respectively. The reference dataset showed that urbanization increased significantly between 2010 and 2012, whereas the area of land used for forests and agriculture decreased. Similar changes in the land-cover types in the reference dataset suggest that urbanization is in progress. Together, these results indicate that aerial orthoimagery and the SegNet model can be used to efficiently detect land-cover changes, such as urbanization, and can be applied for LULC monitoring to promote sustainable land management.

The purpose of this study was to construct artificial intelligence (AI) training datasets based on multi-resolution remote sensing and analyze the results through learning algorithms in an attempt to apply machine learning efficiently to (quasi) real-time changing landcover data. Multi-resolution datasets of landcover at 0.51- and 10-m resolution were constructed from aerial and satellite images obtained from the Sentinel-2 mission. Aerial image data (a total of 49,700 data sets) and satellite image data (300 data sets) were constructed to achieve 50,000 multi-resolution datasets. In addition, raw data were compiled as metadata in JavaScript Objection Notation format for use as reference material. To minimize data errors, a two-step verification process was performed consisting of data refinement and data annotation to improve the quality of the machine learning datasets. SegNet, U-Net, and DeeplabV3+ algorithms were applied to the datasets; the results showed accuracy levels of 71.5%, 77.8%, and 76.3% for aerial image datasets and 88.4%, 91.4%, and 85.8% for satellite image datasets, respectively. Of the landcover categories, the forest category had the highest accuracy. The landcover datasets for AI training constructed in this study provide a helpful reference in the field of landcover classification and change detection using AI. Specifically, the datasets for AI training are applicable to large-scale landcover studies, including those targeting the entirety of Korea.

Urban heat islands and rising global temperatures are increasing the need for passive cooling solutions in densely populated areas. This study examines the effectiveness of heat-reflective paint in reducing surface and indoor temperatures in a 33-year-old apartment complex in Gumi City, South Korea. Using mock structures to simulate real building conditions, this experiment measured surface and indoor temperatures in both treated and untreated groups. Data collected from thermal imaging cameras and data loggers demonstrated significant temperature reductions on the treated walls, with surface temperatures lowered by an average of 2.2 °C to 4.3 °C and peak reductions of up to 15.3 °C. Internally, the treated structures showed temperature reductions between 2.1 °C and 4.1 °C, with the largest difference of 4.14 °C observed during peak heat hours. The results highlight the effectiveness of heat-reflective paint in mitigating heat stress and improving indoor thermal comfort, particularly in regions with high solar exposure. This study aims to support vulnerable populations, such as the elderly and economically disadvantaged residents, by providing a cost-effective, sustainable solution to reduce heat exposure in aging urban buildings, thereby enhancing their quality of life.

With the increasing interest in effective renewable alternative energy sources resulting from the Paris Agreement on Climate Change in 2015, photovoltaic (PV) power generation is attracting attention as a practical measure. In this study, we develop procedures for efficiently monitoring PV panels in a large area and increasing their classification accuracy to enable efficient management of PV panels, an important component of renewable energy generation. To accomplish this, first, the persistent scatterer characteristics (e.g., polarization, imaging module, and topography) of PV panels in SAR images were utilized. Then, we developed a technique for classifying panels over a certain size using the polarization and pulse-scattering characteristics of Sentinel-1. Next, by stacking Sentinel-1 ground range Doppler (GRD) images and comparing them with the surroundings of the same area, the morphological features of PV panels were derived and built as learning data for machine learning. Then, a more precise classification of PV panels was performed by applying these learning data in AI algorithms. When SAR-based AI training data for the same PV panels were used in the YOLOv3 and YOLOv5 algorithms, both algorithms showed high accuracy of over 90%, but there were differences in precision and recall. These findings will enable more efficient monitoring of PV panels, the use of which is expected to increase in the future. In addition, they can serve as a proactive response tool to address environmental problems such as PV panel waste and panels washed away during natural disasters.

Urban heat islands and rising global temperatures are increasing the need for passive cooling solutions in densely populated areas. This study examines the effectiveness of heat-reflective paint in reducing surface and indoor temperatures in a 33-year-old apartment complex in Gumi City, South Korea. Using mock structures to simulate real building conditions, the experiment measured surface and indoor temperatures in both treated and untreated groups. Data collected from thermal imaging cameras and data loggers demonstrated significant temperature reductions on the treated walls, with surface temperatures lowering by 2.2°C to 4.3°C on average, and peak reductions of up to 15.3°C. Internally, the treated structures showed temperature reductions between 2.1°C and 4.1°C, with the largest difference observed during peak heat hours at 4.14°C. The results highlight the effectiveness of heat-reflective paint in mitigating heat stress and improving indoor thermal comfort, particularly in regions with high solar exposure. Importantly, this study aims to benefit vulnerable populations, such as the elderly and economically disadvantaged residents, by providing a cost-effective, sustainable solution to reduce heat exposure in aging urban buildings, thereby enhancing their quality of life.