• Korean Journal of Remote Sensing
• /
• v.36 no.5_3
• /
• pp.1095-1107
• /
• 2020

Flood monitoring using satellite data has been constrained by obtaining satellite images for flood peak and accurately extracting flooded areas from satellite data. Deep learning is a promising method for satellite image classification, yet the potential of deep learning-based flooded area extraction using SAR data remained uncertain, which has advantages in obtaining data, comparing to optical satellite data. This research explores the performance of SegNet and U-Net on image segmentation by extracting flooded areas in the Khorat basin, Mekong river basin, and Cagayan river basin in Thailand, Laos, and the Philippines from Sentinel-1 A/B satellite data. Results show that Global Accuracy, Mean IoU, and Mean BF Score of SegNet are 0.9847, 0.6016, and 0.6467 respectively, whereas those of U-Net are 0.9937, 0.7022, 0.7125. Visual interpretation shows that the classification accuracy of U-Net is higher than SegNet, but overall processing time of SegNet is around three times faster than that of U-Net. It is anticipated that the results of this research could be used when developing deep learning-based flood monitoring models and presenting fully automated flooded area extraction models.

• Journal of odor and indoor environment
• /
• v.17 no.4
• /
• pp.355-361
• /
• 2018

Mold grows more easily when humidity is higher in indoor spaces, and as such is found more often on wetted areas in housing such as walls, toilets, kitchens, and poorly managed spaces. However, there have been few studies that have specifically assessed the level of mold in the indoor spaces of water-damaged housing in the Republic of Korea. We investigated the levels of airborne mold according to the characteristics of water damage types and explored the correlation between the distribution of mold genera and the characteristics of households. Samplings were performed from January 2016 to June 2018 in 97 housing units with water leakage or condensation, or a history of flooding, and in 61 general housing units in the metropolitan and Busan area, respectively. Airborne mold was collected on MEA (Malt extract agar) at flow rate of 100 L/min for 1 min. After collection, the samples were incubated at $25^{\circ}C$ for 120 hours. The cultured samples were counted and corrected using a positive hole conversion table. The samples were then analyzed by single colony culture, DNA extraction, gene amplification, and sequencing. By type of housing, concentrations of airborne mold were highest in flooded housing, followed by water-leaked or highly condensed housings, and then general housing. In more than 50% of water-damaged housing, the level of airborne mold exceeded the guideline of Korea's Ministry of Environment ($500CFU/m^3$). Of particular concern was the fact that the I/O ratio of water-damaged housing was greater than 1, which could indicate that mold damage may occur indoors. The distribution patterns of the fungal species were as follows: Penicillium spp., Cladosporium spp. (14%), Aspergillus spp. (13%) and Alternaria spp. (3%), but significant differences of their levels in indoor spaces were not found. Our findings indicate that high levels of mold damage were found in housing with water damage, and Aspergillus flavus and Penicillium brevicompactum were more dominant in housing with high water activity. Comprehensive management of flooded or water-damaged housing is necessary to reduce fungal exposure.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1709-1722
• /
• 2022

The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model's input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.