Unlocking a Century of Knowledge: How AI is Revolutionizing Land Management

The Challenge: A Mountain of Valuable, but Inaccessible, Data

The Bureau of Land Management (BLM) is the steward of vast swaths of public land, and every decision it makes is backed by a wealth of scientific information, environmental analysis, and planning. Over the decades, this has resulted in a massive knowledge base—over 42,000 documents in the ePlanning register alone. However, this critical information, from Resource Management Plans (RMPs) to environmental impact statements, has traditionally been locked away in unstructured documents and complex GIS files. This makes it incredibly difficult for planners and resource specialists to ensure new projects comply with existing plans, trace monitoring requirements, or even understand the historical context of a decision. The result is a slow, manual, and often inconsistent process that struggles to keep pace with modern demands.

What can AI do for National Land Management Planning and Environmental Analysis?

  • Environmental Analysis and Planning Activities
    • Rapidly read and interpret the history of NEPA analysis and decisions rapidly informing the business workflows of historical precedents including frequency, associated best practices and standards.
    • Enable Land Management Roles and External Customers Research the types of Restrictions and Requirements that can be anticipated NEPA and RMPs prior to submitting their proposals (e.g. Integrate the data from RMP and NEPA documents with geospatial information – local and nationally standardized; Identify and standardize and share best practices and methods of scientifically approaches to the issues and actions)
  • Program Management:
    • Evaluate consistency of planning rules across administrative offices for better practices and management of effects
    • Evaluate consistency of decisions across administrative offices to be better prepared for appeals and litigation for the variety of actions taken
    • Evaluate degree of scientific citations for the variety of NEPA products.
  • Data Standardization:
    • Facilitate the standardization of data used in monitoring, planning and NEPA by identifying similarities in language used historically.

The Solution: Teaching a Computer to Read, Understand, and Map

To tackle this challenge, the BLM embarked on a groundbreaking research and development project to apply Artificial Intelligence (AI) to its land and resource management practices. The team developed and trained a sophisticated AI model to read and understand the complex language of NEPA and planning documents. This model doesn't just recognize keywords; it interprets the context and intent of sentences, classifying them as restrictions, requirements, proposed actions, or geographic locations. In a particularly innovative step, the AI was also trained to read the legal land descriptions (LLDs) written in documents and automatically convert them into interactive GIS maps, providing a visual representation of where an action is set to occur. This links the "what" of a plan to the "where" on the ground.

The Benefits: Faster Decisions, Greater Consistency, and Deeper Insight

The impact of this AI-powered approach has been nothing short of transformative. Where human analysts took many months to review and categorize planning documents, the AI model accomplished the same task in minutes, reading 194 RMPs and identifying nearly 25,000 planning rules in under 25 minutes. This incredible speed allows for rapid analysis of potential conflicts between proposed actions and existing land use rules. By integrating national and local GIS data, the system can instantly flag when a new project might impact a sensitive area, like an eagle's nest or critical habitat, based on the specific buffer distances and restrictions defined in the RMP. This provides BLM staff with readily accessible, high-value knowledge, enabling more efficient, consistent, and scientifically-defensible decisions.

 

Some of the Eye-Catching Outputs

Eye-Catching Outputs

Caption: The AI-powered system integrates national datasets, like Areas of Critical Environmental Concern (ACEC), with local field office data. Here we see national wildlife habitats (pink) and ACECs (yellow) overlaid with local critical habitat data from the Tucson Field Office (blue), providing a comprehensive view of potential resource conflicts. 

  

A key innovation is the AI's ability to read complex legal land descriptions (LLDs) from a Categorical Exclusion (CX) document and automatically generate an accurate GIS map of the project area. This image shows the textual description for a right-of-way and the corresponding map generated by the system, instantly making the document's spatial data usable. 

A comparison showing a legal land description from a document and the resulting map.

The AI system can process historical documents, like this 1978 Mineral Survey map. By extracting and classifying information from such records, the BLM can convert decades of historical data into a searchable, digital format, preserving knowledge and making it accessible for future planning and analysis.

An example of a historical survey map

A Look Under the Hood

The success of this initiative stems from a sophisticated technical architecture that combines Natural Language Processing (NLP) with powerful geospatial analysis tools. Here’s how it works:

  • AI-Powered Reading and Classification: At the core of the system is a trained NLP model that reads unstructured documents (PDFs, Word documents, etc.). It uses a custom vocabulary of over 1,300 phrases to understand the nuances of land management language. The model identifies and classifies key information, such as:

    • Actions and Structures: What is being proposed (e.g., "road construction," "oil and gas lease").

    • Restrictions and Requirements: The rules that govern an action (e.g., "no surface occupancy," "seasonal limitations").

    • Proximity and Time Limits: Specific spatial and temporal constraints (e.g., "within 1.2 miles," "from July through September").

  • From Text to Map: The AI model is uniquely capable of parsing Public Land Survey System (PLSS) legal descriptions from text. It converts these descriptions into a standardized format and uses the BLM's own Cadastral server API to generate the corresponding geometry, which is then displayed as a map layer. This turns a static document reference into a dynamic, queryable spatial feature.

  • Integrated Data Environment: All the classified information, from both documents and GIS files, is ingested into an ElasticSearch database. This central hub allows users to perform complex queries that span across thousands of documents and hundreds of GIS layers simultaneously. A resource specialist can now ask a question like, "Show me all the restrictions for drilling within 10 miles of a specific location," and receive a comprehensive answer in seconds.

Lessons Learned

  • Local Data is a Treasure Trove: Field offices possess invaluable, detailed GIS data. The AI pipeline was designed to ingest this local data without forcing prior standardization, making it immediately useful for national-level analysis.

  • Automation Unlocks History: The ability to rapidly process documents means that the vast history of BLM decisions and plans can be digitized and made searchable, providing unprecedented context for today's decisions.

  • Beyond Search to Understanding: This is more than a simple keyword search. By understanding the relationships between actions, restrictions, and locations, the AI provides a true decision support tool, helping analysts identify conflicts and ensure compliance with complex management plans.