Textomap

Automatically identifies and extracts geographic locations from unstructured natural language text without requiring pre-formatted data or manual annotation. Uses NLP-based entity recognition (likely named entity recognition with geographic gazetteers) to detect place names, addresses, and location references embedded within prose, then maps each extracted location to geographic coordinates via integrated geocoding service. This eliminates the data-cleaning bottleneck where users would normally need to manually parse and structure location data before mapping.

Converts extracted or provided geographic coordinates into embeddable, interactive web maps with pan, zoom, and click-to-inspect functionality. Likely uses a mapping library (Leaflet, Mapbox GL, or Google Maps API) as the rendering engine, with a lightweight template system that applies styling and marker customization based on user-selected themes. Maps are generated as standalone HTML artifacts that can be embedded in web pages, shared via URL, or exported for offline use.

Augments extracted geographic locations with contextual metadata such as place names, administrative boundaries, and user-provided descriptions or tags. The system likely stores location-to-metadata mappings in a database indexed by coordinates, allowing rapid lookup and association of additional information with each map marker. Users can manually add descriptions, categories, or custom fields to locations, which are then displayed in interactive popups or info windows when map viewers click markers.

Manages persistent storage of user-created maps with access control and URL-based sharing. Maps are likely stored in a cloud database (PostgreSQL, MongoDB, or similar) indexed by user account and map ID, with a URL routing system that generates shareable links. The freemium model likely restricts storage quota, number of maps, or marker limits on the free tier, with paid tiers offering higher quotas and additional features like custom domains or private sharing controls.

Applies predefined visual themes to maps, controlling marker appearance, color schemes, basemap selection, and UI layout without requiring CSS or design skills. The system likely maintains a library of theme templates (e.g., 'minimal', 'satellite', 'dark mode') stored as configuration objects that define marker icons, color palettes, and basemap tile sources. Users select a theme from a dropdown, and the system applies the configuration to the map rendering pipeline, updating all visual elements consistently.

Accepts pre-structured location data (CSV, JSON, or spreadsheet formats) and bulk-imports locations into a map without requiring manual entry or text parsing. The system likely includes a schema mapper that allows users to specify which columns contain latitude/longitude, location names, or metadata fields, then validates and imports the data in a single operation. This capability bridges the gap between unstructured text extraction and structured data workflows, allowing users to combine both approaches.

Generates embeddable HTML iframe code that allows users to embed interactive maps into external websites, blogs, or content management systems without hosting or managing the map themselves. The system generates a unique iframe URL pointing to the hosted map, with optional parameters for controlling initial zoom level, center coordinates, or UI element visibility. The iframe is sandboxed to prevent XSS attacks and maintains the interactive functionality of the original map.

Provides a search interface that allows map viewers to find specific locations by name, category, or metadata without manually panning and zooming. The search likely uses client-side full-text indexing (JavaScript-based search) or server-side database queries to match search terms against location names and metadata fields, then highlights or filters matching markers on the map. Filtering may support multiple criteria (e.g., 'show only venues with capacity > 100') if metadata is structured with categorical fields.