Intelligent GIS technology system

Intelligent modeling

● Integrating advanced deep learning methods to construct a deep learning intelligent modeling framework

● Fully utilize lithological data information, mine hidden geological features, and achieve more dimensional expression of geological structures

● Integrating deep learning and sample training to assist in lithology modeling can to some extent solve the modeling bottleneck of complex geological systems

Intelligent mapping

● By utilizing the highly current nature of remote sensing images, we can skip the tedious mapping process and directly generate tile maps from remote sensing images, significantly reducing the production cycle of network maps and improving the timeliness and quality of network map services

● Adopting the tile map rapid generation technology supported by generative adversarial networks, combined with expert mapping knowledge and terrain semantic information constraints, to achieve intelligent conversion and generation of multi-scale map elements, taking into account the multi-scale characteristics of maps, and generating network maps with higher effectiveness

information extraction

● Establish interactive Prompt prompts and use object recognition+SAM segmentation mechanism to achieve large-scale remote sensing image segmentation and object recognition

● Targeting the fusion of multidimensional features in oblique photography, an enhanced Pompt prompt is used based on the SAM large model to achieve semantic segmentation of architectural objects

● Added real-time inference and analysis services for small data volumes, integrating image segmentation and geological map extraction services

● Newly added offline inference analysis service for bulk data, integrating image segmentation function service

● Support OCR recognition of geological text, tables, geological bar charts and other geological information, and can convert the recognized content into text, tables and other types

Intelligent analysis and interaction

● New intelligent analysis module has been added to manage time-consuming AI analysis tasks, with built-in services such as image instance segmentation and semantic segmentation

● New intelligent interaction function module has been added, providing knowledge base management, knowledge base Q&A, large model Q&A, natural language interpretation, tool calling, content generation and other functions for natural language large models

Large model

● Integrating various commonly used big model algorithms such as natural language big model, visual big model, multimodal big model, etc., to empower more industry applications

● Newly added model management tool, providing process management tools for fine-tuning datasets, inference deployment, optimization, evaluation, etc., to facilitate the development of geographic industry-specific large models

● New intelligent agent application construction tools such as prompt engineering, knowledge base, plugins, and rule base have been added, simplifying the process of AI agent/intelligent assistant application construction and development, and improving application development efficiency and convenience

Geographic Knowledge Graph

● Integrated graph database, providing geographic knowledge graph services, realizing functions such as graph directory construction, graph visualization, graph data query and analysis, entity association, etc

● Utilizing semantic networks to provide multidimensional descriptions of geographic concepts, entities, and related relationships, enhancing the dynamic perception, knowledge discovery, and intelligent reasoning capabilities of geographic phenomena

● Extract knowledge from geographic entity data, fuse geographic knowledge based on geographic entity relationships, and achieve visual representation and intelligent retrieval of geographic knowledge graphs

● Using multi-source data such as basic geology, geophysics, geochemistry, multispectral imaging, and geological data, and based on expert experience, sample libraries, knowledge graphs, etc., a knowledge base for mineral exploration is constructed to assist in the analysis of mineralization models

● Based on the development law of geological hazards, a geological hazard susceptibility index system has been constructed, and a knowledge graph model for geological hazard susceptibility evaluation and a digital disaster comprehensive evaluation system have been implemented to better predict and understand the susceptibility of geological hazards