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Tenchijin Inc. — Municipal pipe-leak risk corpus: per-pipe-segment leakage risk scores for Japanese water networks, fusing satellite-measured external drivers (surface temperature, land motion/subsidence) with utility-owned internal pipe attributes (age, thickness, pressure history) and actual survey/repair outcomes. Coverage now spans 100+ municipal water operators, plus adjacent sewer-inspection prioritisation and land-value/agriculture layers on the same COMPASS GIS platform. Institutionally backed Japanese space-tech scale: first Japanese space venture investment sourced from JAXA, execut…
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Municipal pipe-leak risk corpus: per-pipe-segment leakage risk scores for Japanese water networks, fusing satellite-measured external drivers (surface temperature, land motion/subsidence) with utility-owned internal pipe attributes (age, thickness, pressure history) and actual survey/repair outcomes. Coverage now spans 100+ municipal water operators, plus adjacent sewer-inspection prioritisation and land-value/agriculture layers on the same COMPASS GIS platform
From 5 yearsCoverage IndustrialsAsset class Equities
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Tenchijin Inc. — Municipal pipe-leak risk corpus: per-pipe-segment leakage risk scores for Japanese water networks, fusing satellite-measured external drivers (surface temperature, land motion/subsidence) with utility-owned internal pipe attributes (age, thickness, pressure history) and actual survey/repair outcomes. Coverage now spans 100+ municipal water operators, plus adjacent sewer-inspection prioritisation and land-value/agriculture layers on the same COMPASS GIS platform. Institutionally backed Japanese space-tech scale: first Japanese space venture investment sourced from JAXA, execut…
Tenchijin Inc. offers (Alternative, Geospatial) — Target market context is concrete: Japan has over 189,000 km of water pipe older than 40 years and roughly 20,000 new leaks per year. Risk scoring is therefore applied to a pipe network measured in tens of thousands of kilometres across 100+ municipalities, re-scored on each satellite revisit, so the geocoded per-segment risk history plausibly runs to hundreds of millions of segment-epoch records.
Training and benchmarking infrastructure-failure prediction models; physics-ML hybrid models linking subsidence and thermal cycling to pipe rupture; capital-planning and asset-renewal optimisation for aging networks; insurance and catastrophe modelling for water-network failure; validation datasets for satellite land-motion products; reinforcement-learning environments for inspection-route optimisation under budget constraints
The data is with 5 years of history.
Coverage spans Japan, US, UK; Environmental & Facilities Services; alternative, geospatial; equities.
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