This paper presents TerraMori, an Internet of Things (IoT)-based sealed terrarium designed for automated cultivation of Moringa oleifera. The system integrates an ESP32 microcontroller with sensors for temperature, humidity, light, carbon dioxide, oxygen, and soil moisture, together with actuators for ventilation, irrigation, misting, and lighting. Sensor readings are processed in real time and mapped to the Blynk IoT platform, enabling both automated regulation and remote monitoring. A four-week evaluation compared two plants grown inside TerraMori with outdoor controls. The system maintained stable internal conditions within target ranges (temperature 26-38°C, humidity 55–75%, soil moisture 30–60%, CO2450–550 ppm, and O218–21%). TerraMori plants showed improved growth and more stable leaf color values under controlled conditions. Effect size analysis indicated substantial practical differences, although statistical significance was limited by sample size. Overall, TerraMori demonstrates potential as a sustainable, autonomous cultivation solution for urban and resource-constrained environments, with future work focusing on larger trials, longer deployment, and additional objective plant health metrics.

Accurate food intake tracking is key to a healthy lifestyle, especially for managing diet-related illnesses. Traditional self-reporting methods are often unreliable due to memory and portion size estimation errors. This study introduces a new system that uses object detection and depth mapping to measure the volume of Filipino food items. The system uses a custom dataset with images of three food classes taken from top and side views, each annotated with bounding boxes. It employs the YOLOv8 model for accurate object detection and depth estimation to create 3D food models for precise volume calculation. Achieving 95.4% detection accuracy and a 7.8% average volume error, the system shows promise for dietary monitoring applications. Experimental results prove the system's ability to process the custom dataset effectively, achieving accurate object detection and volume estimations. Through the integration of innovative computer vision methods with culturally appropriate food data, this solution provides a promising method for boosting dietary monitoring and nutritional evaluation in the Filipino community setting. While the model performed well on the three predefined food classes, misclassification of non-target items (e.g., Biko) highlights the absence of a background or rejection class in the training dataset.

Urbanization, characterized by the movement of populations from rural to urban areas, is reshaping societies globally. This transformation is driven by economic, social, and environmental factors that make urban areas appealing, offering better jobs, access to healthcare and education, and improved infrastructure. However, urbanization also presents significant challenges, including the Urban Heat Island (UHI) effect, which exacerbates living conditions in densely populated cities by increasing surface and air temperatures compared to surrounding rural areas. This study addresses the UHI phenomenon and heat vulnerability by conducting an Urban Heat Island and Heat Vulnerability Index (HVI) assessment, incorporating ground validation, for Makati City, Philippines. The UHI Map was developed using Landsat 8 satellite Land Surface Temperature (LST) data. The HVI Map was developed using Landsat 8 Data Product and Demographic data to identify areas at higher risk of adverse heat-related effects. Each indicator scores were normalized, scored, and aggregated to calculate an HVI score for each barangay. Ground validation was performed to verify the results from the UHI and HVI maps. A prototype device was designed, consisting of sensors for ambient temperature and relative humidity, a memory device for data storage, and a battery with a one-month operational lifespan. Three devices were deployed in barangays identified by the HVI Map: two in the most vulnerable barangays and one in the least vulnerable barangay. Over a month, the devices recorded temperature and humidity data, which were analysed to corroborate the maps' findings. The UHI and HVI maps identified areas within Makati City that are most susceptible to heat-related health impacts, offering critical insights for policymakers and urban planners. The integration of socio-economic and environmental factors ensures a holistic understanding of heat vulnerability, enabling evidence-based strategies to mitigate risks. The findings support urban planning efforts aimed at reducing heat exposure through increased vegetation, improved building designs, and the development of heat-resilient infrastructure. Additionally, this research contributes to achieving Sustainable Development Goal (SDG) 11, Sustainable Cities and Communities, by promoting strategies for creating inclusive, safe, and sustainable urban environments.

This study presents the implementation of an onboard Image Classification Unit (ICU) for the AlAinSat-1 CubeSat, aiming to enhance its autonomy and data processing capabilities. The focus is on integrating a trained CNN model onto AlAinSat-1’s STM32 microcontroller.The designed system employs TensorFlow models trained for image classification tasks relevant to CubeSat missions, such as target accuracy detection, to determine if the image captures the required target, and image quality assessment to estimate cloud cover percentage.The integration process onto the STM32 microcontroller involves addressing the resource constraints inherent in CubeSat platforms. The paper details the optimization techniques applied to adapt the model to the STM32 architecture, ensuring efficient execution within the available hardware resources.Key aspects covered in the study include hardware-software co-design considerations, addressing memory and computational limitations, and optimizing mission duration for power consumption efficiency. Additionally, the development of a reliable communication interface between the onboard Image Classification Unit (ICU) and CubeSat’s main control system is discussed to facilitate seamless integration into the overall satellite architecture.The presented implementation enables CubeSats to perform onboard image classification tasks, reducing the need for constant communication with ground stations and enabling quicker response times for mission decisions. This research contributes to the growing field of embedded machine learning applications in spaceborne remote sensing systems, showcasing the feasibility and benefits of incorporating image processing capabilities on resource-constrained platforms.

Crime poses a significant concern for personal safety, especially in densely populated areas like Manila City, Philippines. Despite ongoing efforts to combat crime, individual safety remains a pressing issue. This study aims to address this problem by developing an innovative crime-mapping solution using a GIS-based system. The objective is to design a mapping system that visually represents crime density through color-coded indicators. The findings demonstrate the system's capability to generate information across different time points and accurately forecast annual crime rates. Throughout the testing phase, the system consistently delivers reliable outcomes, validated using data from the Manila Police District Headquarters. The findings of this research have practical application for law enforcement agencies and urban planners in Manila City, as the developed GIS-based system can assist in allocating resources and implementing targeted crime prevention strategies. A crime risk forecasting system, considering factors such as crime type, location, timing, day, and frequency, can anticipate potential criminal activity. The developed GIS-based system provides a valuable tool for crime forecasting and enhancing public safety in Manila City.

Urban microclimatic conditions in fast-developing Metro Manila continue to move away from comfort due to urban warming generated by the built environment. One of the most familiar effects of urbanization is the Urban Heat Island (UHI) phenomenon. Urban heat islands (UHIs) are meteorological effects of urbanization that cause the air temperature in urban regions to be higher than in non-urban regions. An increase in urban temperature due to urbanization has been observed and studied worldwide. The need to design sustainable, comfortable, and environmentally friendly urban settings has grown because of growing urbanization and the negative consequences of climate change. This study aims to design and develop a low-cost sensor terminal that is equipped with different sensors such as anemometer, humidity, temperature sensors to measure meteorological parameters in various urban green spaces utilizing LoRaWAN for data transmission.

The Philippines’ mangrove forests offer numerous ecosystem goods, services, and protection to coastal populations. However, in 2018, illegal and massive mangrove cutting within the areas of Bulacan was reported that caused mangrove extent changes across the province. In 2020, an initiative to plant mangrove seedlings over 76 hectares in Bulacan was taken to address perennial flooding in the villages affected by deforestation. Although mangrove cover maps for the country already exist, analysis of mangrove cover changes was limited by the challenge of acquiring cloud-free optical satellite data. This study aims to assess the spatial distribution of mangrove forests in the province of Bulacan using the combination of different bands of Landsat-8 and Sentinel-1 images arranged in time series from 2016 to 2020. A machine learning algorithm will be used to classify the images into two classes - mangroves and non–mangroves. The resultant mangrove forest maps will be used to assist the decision-making processes for rehabilitation and conservation efforts currently needed to protect and restore the mangrove forests in Bulacan province.

Climate change has been affecting human life since more than a decade ago. IEEE GRSS through IEEE GRSS Student Grand Challenge program has gathered students from several universities including Telkom University and Kyushu Institute of Technology to develop a CubeSat payload for climate change monitoring mission. Both teams were working on the development of the experimental remote sensing payload that is integrated with an on-board classification system. The payload is equipped with a small serial camera and two microcontrollers for controlling and for applying the classification algorithm. The ultimate target of this payload is detecting cloud coverage in the images. It is an indication of environmental change. This project has yielded a payload called Locana payload. It brings an Arducam OV5642 and two microcontrollers ATSAMD21 and SMT32F7 as the camera controller and cloud classification processor consecutively. This project has given a priceless educational experience for both teams, they were separated geographically but they were working on the same PCB board. The hardware and software design and integration have been carried out utilizing online meetings and remote access due to the pandemic.

AlainSat-1 is an educational and scientific nanosatellite project that was initiated in late 2019 by the IEEE Geoscience and Remote Sensing Society (GRSS) along with National Space Science and Technology Center (NSSTC) of UAE University in the frame of the 2nd Student Grand Challenge [1]. The project involves close collaboration between four international universities to design, build, test and launch a remote sensing CubeSat.The spacecraft is a 3U CubeSat that has a mass of around 4 Kgs. The spacecraft has an active 3-axis control system capable of attitude determination and control to less than one degree. Two communications systems will be used on-board: a UHF System and an S-Band System. The project has passed the Critical Design Review (CDR) stage and is currently in the assembly and integration phase. The satellite is currently planned for launch to a sun-synchronous orbit on-board a Falcon 9 rocket in the second quarter of 2024.

This paper presents the in-orbit results of a commercial-off-the-shelf (COTS) imaging payload deployed on the BIRDS-4 1U CubeSat constellation. The BIRDS-4 Satellite project, hosted at the Kyushu Institute of Technology, aims to develop Paraguay's first satellite while enhancing the standardized bus system for future missions. The imaging payload, utilizing proven components, consists of a microcontroller, dedicated flash memory, and a camera module with a modified lens. Mission-specific software complements the hardware, enabling various mission modes. The imaging payload achieves a 5MP camera resolution, a 125-degree diagonal field of view, a 1000km ground swath, and a 300m ground resolution. The paper provides detailed information about the payload design, mission modes, and specifications. The flight model successfully underwent ground tests, including thermal vacuum, vibration, and long-duration tests, validating its functionality. In orbit, the payload executed several mission modes, and images were downloaded through the BIRDS-4 ground station network. While the mission achieved medium success, capturing low-resolution images of Earth with identifiable land masses and images during release from the International Space Station (ISS), lessons learned were identified for future missions, such as improved satellite pointing, extended access time for image transfer, and enhanced mission execution commands. Noteworthy images, including Salar de Uyuni in Bolivia, the Parana River in Latin America, Shikoku Island in Japan, and a photo of the ISS and Earth, were captured and distributed for promotional and outreach activities. The obtained images, totaling 89 with 81 successfully downloaded, range from 3kB to 99kB in size, comprising 2.33 MB of data. The images were categorized into Earth, Solar Flare, Space, and Unknown. The paper concludes by highlighting the application of lessons learned in the subsequent iteration of the satellite developed locally by a new team from the Philippines.

Climate change has occurred as a result of human activities. It can trigger unexpected disasters such as floods or drought. Further severe events may be avoided by monitoring climate change. A method to do that is monitoring the cloud coverage in some areas. In this paper, the development of a CubeSat payload that can monitor cloud coverage is presented. It contains a COTS camera module, microcontrollers, and a cloud classification algorithm. This payload is a joint research between Telkom University and Kyushu Institute of Technology under IEEE GRSS 2nd Student Grand Challenge. This payload has been implemented and tested and the result is the payload able to capture images in a long period and classify the cloud feature of each of them. Currently, it has reached the flight model stage and is ready to get further space environmental tests.

Although the cost of development is cheap, cube satellites are limited in power, size, and downlink capabilities. By optimizing algorithms and the hardware these algorithms run, one overcomes these limitations, thus, allowing more missions to run and more data to be collected from it. Images, for example, are relatively big in size and if the satellite were able to know which image data to downlink, it could save a lot of time and resources. For this purpose, a cloud detection algorithm based on the U-net architecture was developed using the TensorFlow library. This model will be trained using a dataset of 15,263 images taken from the Landsat 8 satellite while the SPARCS cloud assessment dataset was used to evaluate the model on images it was not trained on. To limit the size of the input data, only the RGB band was used. After optimizing the model's parameters, the model shows that it achieved an overall accuracy of ∼85%. Furthermore, testing of the same model on images of lower resolution taken from CubeSats showed that it still was fairly accurate and would manage to work in most CubeSats that would only be able to take low resolution images. The model was then quantized and was then converted to a C code 8 bytes array using the TensorFlow Lite library to reduce its size and operation. It is then implemented inside a STM32F746BGT6 microcontroller which can then be used by cube satellites to detect clouds from the images it would take. This module is the Image Classification Unit (ICU). As a proof of concept, this ICU will be implemented inside a 3U CubeSats mission developed at the National Space Science and Technology Center, UAE.

This paper aims to assess the relationship between atmospheric temperature and land surface temperature (LST) in Manila City using satellite and synoptic weather station data from 2014 to 2018. Manila is the capital and second-largest city of the Philippines and one of the most densely populated cities in the world, so it is important to analyze the said parameters to give a better picture of the city heat situation. Atmospheric temperature is taken from the synoptic weather station measurements at Manila Port Area while land surface temperature was derived from MODIS satellite data at the same point. By analyzing the data, trend changes of the parameters can be obtained and observed. Using statistical correlation analysis, the relationship between the parameters were also established. Data shows that high air temperature measurements fall between March and May. Correlation analysis shows that Temperature measurement from weather data (Min, Max, Mean) and derived values from satellite data (LST Day & Night) mostly have a high positive correlation.

Feedback Control and Monitoring System for a Potable Rainwater and Groundwater Harvester aims to develop a device that will be able to measure the parameters of water that has pass through its filtration system. The system has a container for the output of the filter where parameters for potable drinking water are tested if standards for Class AA or potable water are met. If the water fails to meet the standards, the water from the container will be recirculated to the filter until it attains the standards. If the set standards are met, the water will be transferred to the final container and is ready for harvesting. The researches gathered 30 trials for each water input such as rainwater and groundwater. For each trial, final readings of the parameters of the water that passed the standards are recorded. The parameters consist of pH, turbidity, conductivity, TDS, temperature, and ORP which are the chemical aspects of the water. The data between the input and output of the system will be compared using t-test to determine if there is a significant change that is within the acceptable range of standards. The water results will be validated further by testing the water output in a laboratory that is certified by the International Organization for Standardization (ISO). The output will be then compared again to the output of the laboratory test to ensure water quality.