リモートセンシングＥ

[Oct. 6, 2026] Introduction to remote sensing (Takeuchi) online [Oct. 9, 2026] Introduction to photogrammetry and LiDAR (Takeuchi) in person [Oct. 13, 2026] Introduction to multi-hyper-super spectral remote sensing (Soni) in person [Oct. 16, 2026] Special lecture: Remote sensing application of CH4 rice project in Bangladesh (Rahe) online [Oct. 20, 2026] Introduction to AI and application to remote sensing (Rishi) in person [Oct. 23, 2026] Special lecture: Remote sensing application of solar PV project in Philippines (Jeark) online [Oct. 27, 2026] Remote sensing application of mangrove mapping project in Indonesia (Soni) in person [Oct. 30, 2026] Special lecture: Remote sensing application of oil palm plantation project in Malaysia (Samitha) online [Nov. 6, 2026] Imaging radar principles and application for civil infrastructure and disaster monitoring (Khin) [Nov. 10, 2026] Hands-on-training on SAR data analysis for earthquake damage assessment (cont’d) (Khin) in person [Nov. 13, 2026] Hands-on-training on SAR data analysis for earthquake damage assessment (cont’d) (Khin) in person [Nov. 17, 2026] Final group presentation of hands-on-training outcomes in person

Komaba-II campus, As-301 and zoom

Final report: 40% Group presentation: 60%

Prerequisites: A basic understanding of GIS (Geographic Information Systems) is recommended but not required. Group Work: This course relies heavily on collaborative learning. Students must be prepared to work in assigned groups for both field measurements (TLS, PM2.5) and data analysis. International Participation: As lectures feature guest speakers from across Asia, all sessions and materials will be conducted in English. Software Requirements: Students should bring a laptop capable of running data processing software (instructions for installation, e.g., QGIS or CloudCompare, will be provided in the first session).

Given Professor Takeuchi's extensive involvement with international organizations and large-scale research projects across Asia, this course is designed not just as a technical exercise, but as a direct reflection of real-world problem-solving. Here is how that practical, international experience directly enriches your learning: 1. Access to "Living" Case Studies Rather than using generic textbook examples, the course utilizes data and challenges from active projects. Whether it is AWD in Bangladesh or Infrastructure Monitoring in the Philippines, you are working on the same problems currently being discussed at the ministerial and international levels (such as at the UN or JICA). 2. Integration of Global Standards Working with international organizations requires high standards for data accuracy and reporting. Relevance: The way you are taught to process TLS (3D Scanning) and SAR data in this course mirrors the rigorous "standard operating procedures" used in global disaster response and environmental accounting. 3. The "Human" Side of Remote Sensing Prof. Takeuchi’s experience teaches that data is only useful if it serves people. Social Sensing & Multi-source Fusion: This specific focus (Lecture 08) reflects the modern international shift toward Human-Centric Remote Sensing. You will learn how to integrate satellite data with social dynamics, a skill highly valued in international NGOs and development agencies. 4. Direct Connection to International Experts The guest lecturers (Prof. Jeark, Prof. Rahedul, etc.) are not just academic names; they are part of a cross-border research network. Value: This gives you a unique "insider" look at how regional cooperation works in science. You aren't just learning a tool; you are seeing how that tool is used to negotiate climate policy and disaster resilience across borders. 5. Ground-Truth Reliability Because Prof. Takeuchi has spent significant time in the field across Asia, the course emphasizes validation. Field Work (TLS/PM2.5): The reason we go out on the Komaba-II campus is to instill the "field-first" mindset. In international research, a satellite image is only as good as the ground-truth data supporting it. You will learn to be a scientist who trusts the data because you know how it was collected. Student Takeaway By the end of this course, you will have a portfolio that reflects the needs of the modern global workforce. You won't just be a "software operator"; you will be a practitioner capable of using geoinformatics to address the Sustainable Development Goals (SDGs) in a complex, real-world environment.