Monitoring of electrical transmission towers (TTs) is required to maintain the integrity of power lines. One major challenge is monitoring vegetation encroachment that can cause power interruption. Most of the current monitoring techniques use unmanned aerial vehicles (UAV) and airborne photography as an observation medium. However, these methods are expensive and not practical for monitoring wide areas. In this paper, we introduced a new method for monitoring the power line corridor from satellite imagery. The proposed method consists of two stages. In the first stage, we used the existing state-of-the-art RetinaNet deep learning (DL) model to detect the locations of the TTs from satellite imagery. A routing algorithm has been developed to create a path between every adjacent detected TT. In addition to the routing algorithm, a corridor identification algorithm has been established for extracting the power line corridor area. In the second stage, the k-mean clustering algorithm has been used to highlight the VE regions within the power line corridor area after converting the target satellite image into hue, saturation, and value (HSV) color space. The proposed monitoring system was able to detect TTs from satellite imagery with a mean average precision (mAP) of 72.45% for an Intersection of Union (IoU) threshold of 0.5 and 85.21% for IoU threshold of 0.3. Also, the monitoring system was able to successfully discriminate high-and low-density vegetation regions within the power line corridor area.INDEX TERMS Deep learning, K-mean, satellite images, transmission tower detection.

The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4 mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The measured power consumption of the developed WSTx was 0.02109 mW during the sleep mode and 11.1 mW during the operation mode. The harvesting system can harvest energy up to 1.2 mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%.

The current revolution in communication and information technology is facilitating the Internet of Things (IoT) infrastructure. Wireless Sensor Networks (WSN) are a broad category of IoT applications. However, power management in WSN poses a significant challenge when the WSN is required to operate for a long duration without the presence of a consistent power source. In this paper, we develop a batteryless, ultra-low-power Wireless Sensor Transmission Unit (WSTx) depending on the solar-energy harvester and LoRa technology. We investigate the feasibility of harvesting ambient indoor light using polycrystalline photovoltaic (PV) cells with a maximum power of 1.4mW. The study provides comprehensive power management design details and a description of the anticipated challenges. The power consumption of the developed WSTx was 21.09µW during the sleep mode and 11.1mW during the operation mode. The harvesting system can harvest energy up to 1.2mW per second, where the harvested energy can power the WSTx for six hours with a maximum power efficiency of 85.714%.

Land subsidence is a geomorphological event that affects Earth’s structure and physiognomy. This phenomenon occurs when the groundwater volume changes and results in the movement and sinking of sediment. Several studies have been conducted to identify major causes or factors that may lead to land subsidence. It was found that land subsidence intensity is influenced by several factors, i.e. terrain slope and aspect, land use, soil moisture content, and distance to a river. Population density contributes to continuous changes in land use. Deep investigation of factors that contribute to land subsidence such as population density is important. This study investigated the relationship between land subsidence and population density contributing to continuous land-use changes. The study area was a highly populated slum area along the Musi River in Palembang, Indonesia. Factors that have high contribution to land subsidence were considered in developing a land subsidence susceptibility map. Susceptibility analysis was done using the Analytical Hierarchy Process (AHP) method. Land subsidence features were associated with slum features and the result revealed a significantly high correlation (r = 0.844) between actual land subsidence areas and the developed susceptibility map.

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