A web-based teaching platform built on one real flood case — Sentinel-1A SAR, 26 November 2025, Banda Aceh. Every figure is a live model output; every step from raw backscatter to flood mask is reproducible in the browser.
A teaching interface organized around workflow rather than a pile of slides. Each case follows one path: raw data, then method, then interpretation and discussion. It suits classroom demonstration, student practice, and CATCON competition review.
Before we look at model predictions, spend ninety seconds with the underlying physics. Radar does not see colour or brightness the way a camera does — it measures how much of its own microwave pulse bounces straight back.
Figure 1aVertical transmit, vertical receive. Most sensitive to smooth surfaces. Water = black, urban = bright.
Figure 1bVertical transmit, horizontal receive. Sensitive to volume scattering in vegetation.
Figure 1cA common false-colour composite. Green highlights vegetation volume scattering.
Three Sentinel-1A acquisitions of the same Aceh coast. Move left to right to follow the event: dry baseline, approaching weather, and co-event inundation.
Figure 2a Baseline. Dry paddy fields, full river discharge, normal coastal outline.
Figure 2b Before landfall. Early moisture accumulation in low-lying areas visible as darker patches.
Figure 2c Peak inundation captured during ascending pass. Large dark zones mark new standing water.
All three scenes are processed with identical radiometric terrain correction (SNAP). What changes is the physical world on the ground, not the sensor.
The model learned floods from KuroSiwo
(2023). This Sentinel-1A scene is from
26 Nov 2025 — after the cutoff —
and, by signal, further out still: Sumatra’s wet vegetation pushes VH
backscatter 5–8× brighter than the training mean.
With stock preprocessing, 71 % of VH pixels saturate
and the flood all but vanishes. One inference-time knob —
clamp = 0.30 — recovers it with the same weights.
training defaultstock preprocessing — flood missed
clamp = 0.30one knob — flood recoveredCompare the CS-Mamba flood prediction under four input-scaling strategies, using the same Sentinel-1A scene over the Aceh coast. Each view below is a real pre-computed model output — click through the tabs and switch the configuration to see what changes.
Each figure below is a three-panel comparison: prediction A, prediction B, and the pixel-wise difference. Red marks pixels classified as flood by A but not B. Blue marks the reverse. The further two configurations are apart, the noisier the difference panel.
At 84.1% agreement, most of the disagreement is blue — new flood that the training-time clamp missed entirely.
94.6% agreement. A conservative clamp captures the main flood footprint but shrinks the extent.
90.5% agreement. An aggressive clamp starts dropping real floods while over-estimating permanent water.
Input clamping bounds the SAR backscatter before normalization. A tight clamp (0.15) truncates bright returns and under-detects floods. A loose clamp (0.5) preserves bright reflectors but washes out the water signature. The sweet spot at 0.3 balances information loss against signal-to-noise — and more than doubles the recovered flood area compared to the training-time setting.
/ BEHIND THE CURTAIN See the full pipeline that produced these maps 6 stages · SNAP → tiles → stats → CS-Mamba → validation · including a scrubbable training trajectoryThose maps are precomputed. This runs CS-Mamba on one 224² tile right now, in your browser (WebGPU/wasm) — no server. Full live workbench → /studio
All in your browser, no server. See what a tile is made of, drag the clamp and watch the flood signal vanish and return, then click the agreement matrix to see where the four configs disagree — the uncertainty map.
Each KuroSiwo-format tile stacks 6 bands — VV + VH at three acquisition times: pre-event 1 (21 Oct, baseline), pre-event 2 (2 Nov, approach) and co-event (26 Nov, the flood). Here are the three VV composites of the scene — read left to right and the flood appears.
Each tile the model reads is a 224 × 224 px crop of these, stacking all 6 bands (VV + VH at each of the three dates). About 911 such tiles make up the Banda Aceh test split.
Every bar is a VH backscatter bucket. Everything to the right of your clamp value gets clipped to the ceiling — identical to the model. Find the clamp that keeps the flood tail visible without drowning in speckle. This one runs entirely in your browser.
No ground truth exists for Banda Aceh on 2025-11-26, so we triangulate: measure the pixel-for-pixel agreement between every pair of configurations. Low numbers are not wrong — they're the teaching signal. Clicking a cell pulls up the real disagreement map.
The platform is built by a compact group combining remote sensing research, teaching practice, and web engineering — optimizing for reproducibility and classroom fit rather than scale.
IRIDeS, Tohoku University
Research interests in photogrammetry, remote sensing, computer vision and machine learning. Works on aerial/satellite image processing, DEM/DSM generation, stereo matching and change detection for urban monitoring and disaster science.
IRIDeS, Tohoku University
Research student at the International Research Institute of Disaster Science. Focus on AI-driven disaster analysis, flood mapping and depth estimation, integrating multi-source remote sensing with physical modelling.
OSCARS Lab, The University of Tokyo
Ph.D. candidate at the Graduate School of Information Science and Technology. Research covers adversarial robustness, deepfake detection, privacy-preserving ML and other AI-safety topics.
Center for Spatial Information Science, The University of Tokyo
Professor Emeritus of The University of Tokyo and Vice President of Reitaku University. Pioneering researcher in spatial information science, GIS, and geospatial data analysis with extensive contributions to human activity mapping and disaster applications.
IRIDeS, Tohoku University
Director of the International Research Institute of Disaster Science (IRIDeS). Professor specializing in disaster geo-informatics, remote sensing approaches for disaster impact identification, and tsunami damage detection.
Not just a contact form — this page is part of the platform's pedagogical loop. Students can ask questions, teachers can collect needs, partners can submit collaboration intent.
For concrete questions and methodological doubts students encounter in practice.
To request sample datasets, practice materials, or case support.
For course collaboration, case co-authoring, or thematic expansion discussions.
Submissions are sent to the backend /inquiries endpoint and stored
in the D1 database.
