Cooperative Agreement for affiliated Partner with the Chesapeake Watershed Cooperative Ecosystem Studies Unit | G26AS00149

FAQs: USGS CESU Cooperative Agreement (Chesapeake Watershed CESU) - GeoAI Landform Mapping, Ontology/Knowledge Graphs, and GNIS Summit Accuracy

1) What is this funding opportunity?

This is a US Geological Survey (USGS) discretionary funding opportunity issued as a cooperative agreement under the Cooperative Ecosystem Studies Units (CESU) Program. It is a science and technology research and development effort (CFDA 15.808) focused on applied research and technical advancement that USGS can collaborate on directly.

2) What is the opportunity number?

The opportunity number is G26AS00149.

3) What is the application deadline?

The listed application deadline is July 24, 2026.

4) What type of award is being offered?

The award is expected to be made as a cooperative agreement under the CESU Program.

5) What does it mean that this is a cooperative agreement?

In this context, the cooperative agreement structure typically involves substantial USGS involvement during the project. The work is framed as something USGS can collaborate on directly, rather than a project carried out entirely independently by the recipient.

6) Is this a competitive grant open to the general public?

No. Eligibility is restricted to organizations that are already participating partners in the Chesapeake Watershed CESU.

7) Who is eligible to apply?

Only organizations that are current participating partners in the Chesapeake Watershed CESU are eligible. The eligible applicant category is listed as "Others," but the defining requirement provided is existing Chesapeake Watershed CESU partner status.

8) If my organization is not a Chesapeake Watershed CESU partner, can we apply?

Based on the eligibility restriction provided, no. The opportunity is specifically limited to existing participating partners in the Chesapeake Watershed CESU.

9) How many awards does USGS expect to make?

This opportunity indicates a single expected award.

10) What is the maximum funding amount available?

The award ceiling is $29,994.

11) What does the award ceiling suggest about project scale?

The stated ceiling indicates a relatively small, focused effort. The description aligns with a pilot, proof-of-concept, or narrowly scoped technical enhancement rather than a large multi-year program.

12) What is the main purpose of the project?

The work supports applied research and technical advancement in geospatial science, data engineering, and elevation-driven mapping, carried out in collaboration with USGS through the CESU cooperative agreement structure.

13) What are the core technical goals of the project?

The scope centers on three closely related goals: (1) advancing GeoAI-enabled automated landform mapping, (2) expanding the Landform Reference Ontology using knowledge graphs, and (3) improving the positional accuracy of summit features in the Geographic Names Information System (GNIS) using lidar-derived elevation datasets.

14) What is meant by "GeoAI-enabled automated landform mapping"?

It refers to using modern machine learning or artificial intelligence methods to detect, classify, and map landforms in a more automated and scalable way than traditional manual interpretation.

15) What is the Landform Reference Ontology work about?

This component focuses on formalizing landform concepts, their definitions, and relationships in a structured ontology, then implementing or extending that ontology through knowledge-graph approaches so landform information can be better organized, linked, and queried across datasets and applications.

16) What does "knowledge graphs" mean in the context of this project?

In this opportunity, knowledge graphs are described as the approach used to implement or extend the Landform Reference Ontology so landform concepts and relationships can be represented in a connected, queryable structure that improves how information is linked across datasets and applications.

17) What is the GNIS summit feature accuracy improvement work?

This goal targets improving the positional accuracy of summit features in GNIS by leveraging lidar-derived elevation datasets. The intent is to use high-resolution elevation models derived from lidar to better locate summit points and correct or refine existing GNIS coordinates.

18) What data source is specifically mentioned for improving summit positions?

The opportunity specifically mentions lidar-derived elevation datasets (high-resolution elevation models derived from lidar).

19) Which national dataset is being improved in this project?

The opportunity specifically references the Geographic Names Information System (GNIS), focusing on summit features.

20) How are the three project goals related?

They sit at the intersection of geospatial science, data engineering, and elevation-driven mapping: automated landform mapping (GeoAI) benefits from well-structured concepts and relationships (ontology/knowledge graphs), and elevation-driven methods (lidar-derived models) support practical accuracy improvements to summit feature locations in GNIS.

21) What CFDA number is associated with this opportunity?

The work is positioned as science and technology research and development under CFDA 15.808.

22) Is USGS expected to collaborate directly on the technical work?

Yes. The opportunity is intended to support applied research and technical advancement that USGS can collaborate on directly through the cooperative agreement structure, which typically involves substantial agency involvement during the project.

23) What is the overall outcome this opportunity is trying to achieve?

Overall, the opportunity supports a CESU-affiliated research team working with USGS to (1) improve automated landform mapping workflows using GeoAI, (2) strengthen the conceptual data model for landforms via ontology and knowledge graphs, and (3) deliver a practical data-quality improvement to GNIS by using authoritative high-resolution lidar elevation surfaces to more accurately position summit features.