Discovery · side-by-side comparison
NASA and DOI
Similarity 0.49 (moderate). Moderate: overlapping task language; often related functions in different settings. Classified as Similar use case: The descriptions overlap. The inventory alone cannot show whether either system could be shared.
Why they matched
| Component | Contribution to score |
|---|---|
| Problem description | +0.373 |
| Same topic area | +0.060 |
| Shared operational function | +0.052 |
| Total | 0.486 |
Most distinctive shared terms: satellite imagery, imagery, satellite, mapping, automated, detection
| Field | Deep Learning for Flood Mapping (DELTA) | Deep Learning based image segmentation [2024 INV#WO0000000107975] |
|---|---|---|
| Agency |  NASANational Aeronautics and Space Administration |  DOIDepartment of the Interior |
| Bureau | ARC: Ames Research Center | USGS |
| Stage | Retired | Pilot |
| Topic | Science | Science |
| AI classification | Computer vision | Classical / predictive ML |
| Sourcing | Not reported | Developed in-house |
| Vendor (reported) | Not reported | Not reported |
| System name | Not reported | Not reported |
| Problem | DELTA simplifies machine learning for satellite imagery. | Machine Learning based shoreline detection and mapping, automated data suitability analyses from satellite imagery |
| Outputs | monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle | shoreline mapping |
| Benefits | Remotely sensed imagery is increasingly used by emergency managers to monitor and map the impact of flood events to support preparedness, response, and critical decision making throughout the flood event lifecycle. To reduce latency in delivery of imagery-derived information, ensure consistent and reliably derived map products, and facilitate processing of an increasing volume of remote sensed data-streams, automated flood mapping workflows are needed. A joint USGS-NASA-Univ. Alabama initiative developed DELTA and applied it to automatic near-real time flood detection, using multiple sources of satellite imagery for use in disaster response. | time savings |
| Data description | Not reported | public satellite imagery |
| PII | Not reported | No |
| ATO | Not reported | No |
| Public code | Not reported | Not reported |
Exploratory reuse assessment
What the inventory can and cannot show| Functional similarity | moderate (0.49) | Some support |
| Inputs and outputs | Both describe outputs; compare below | Some support |
| Mission or administrative function | Image and video analysis | Some support |
| Documentation available | Code: none linked · System names: Not in inventory | Weak or missing |
| Technical uncertainties | AI classification: different; sourcing: unknown / in_house | Verify outside inventory |
| Data-access constraints | Data descriptions: Not in inventory / Reported | Verify outside inventory |
| Privacy and security | PII: blank / no · ATO: blank / no | Verify outside inventory |
| Legal or policy constraints | Not in inventory. Requires independent verification. | Verify outside inventory |
No pair is marked as a validated reuse opportunity. That needs technical, legal and operational evidence beyond this inventory.
Questions for a human reviewer
- Do NASA and DOI solve the same problem for the same kind of user, or only use similar words?
- Are the inputs (data types, formats, volumes) compatible?
- Does either system's Authorization to Operate cover use by another agency?