Autonomous GIS: the next-generation AI-powered GIS

Li, Zhenlong; Ning, Huan

doi:10.1080/17538947.2023.2278895

Cited by 17 publications

(2 citation statements)

References 20 publications

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“…Recently, the advent of Large Language Models (LLMs) has ushered in a new era of possibilities for geospatial science. LLMs, exemplified by models like GPT-4, have the capacity to understand and generate text-based descriptions of geospatial data, thus enhancing geospatial analyses [8,9]. These models are proving instrumental in AI-based Spatial Data Analysis, enabling the automated interpretation of geospatial information from text data [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery

Zeng,

Boehm

2024

IJGI

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This study investigates the efficacy of an open vocabulary, multi-modal, foundation model for the semantic segmentation of images from complex urban street scenes. Unlike traditional models reliant on predefined category sets, Grounded SAM uses arbitrary textual inputs for category definition, offering enhanced flexibility and adaptability. The model’s performance was evaluated across single and multiple category tasks using the benchmark datasets Cityscapes, BDD100K, GTA5, and KITTI. The study focused on the impact of textual input refinement and the challenges of classifying visually similar categories. Results indicate strong performance in single-category segmentation but highlighted difficulties in multi-category scenarios, particularly with categories bearing close textual or visual resemblances. Adjustments in textual prompts significantly improved detection accuracy, though challenges persisted in distinguishing between visually similar objects such as buses and trains. Comparative analysis with state-of-the-art models revealed Grounded SAM’s competitive performance, particularly notable given its direct inference capability without extensive dataset-specific training. This feature is advantageous for resource-limited applications. The study concludes that while open vocabulary models such as Grounded SAM mark a significant advancement in semantic segmentation, further improvements in integrating image and text processing are essential for better performance in complex scenarios.

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Section: Introductionmentioning

confidence: 99%

Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery

Zeng,

Boehm

2024

IJGI

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“…Others like Tao and Xu (2023) examined the capability of ChatGPT in different map making tasks (e.g., thematic and mental maps) based on either publicly available geographical data or conversation-based textual descriptions of geographic space. Similarly, Li and Ning (2023) introduced an autonomous GIS prototype by leveraging LLMs for tasks like geographical data collection, analysis, and visualization through natural language prompts. Some are calling this GeoQA (Geographic Question Answering, Feng et al, 2023) whereby researchers utilize LLMs to answer geographic questions in natural language.…”

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Exploring the new frontier of information extraction through large language models in urban analytics

Crooks,

Chen

2024

Environment and Planning B: Urban Analytics and City Science

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The growing interest in artificial intelligence (AI) and Large Language Models (LLMs) in particular, such as Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2018;Brown et al., 2020;Ouyang et al., 2022), have been gaining more and more interest in both academia and with the public at large. While the results from such LLMs might seem plausible, they can also be questionable or totally incorrect as discussed in the past editorial by Batty (2023) who suggested that "we need to combine AI with our own knowledge and intuitions." Similar notions have been suggested elsewhere by Fu et al. ( 2023) who compared ChatGPT from OpenAI with manual evaluations of climate change plans and noticed that the machine-generated evaluations struggled with planning-specific jargon.Nonetheless, over the last year, LMMs have started to be introduced and utilized in GIScience. This is especially the case as newer versions of ChatGPT have been released with more advanced capabilities, including more robust reasoning, longer context windows and multimodal functionalities (e.g., text and images). These improvements have enabled users using natural language to interact with the system for diverse tasks, ranging from translation, classification, image, and code generation (e.g., GitHub's Copilot) to information retrieval, and mapping. For example, Hu et al. ( 2023) combined geographical knowledge with GPT models to recognize geographical location descriptions in social media posts for supporting disaster response and management while Jang et al. ( 2023) used ChatGPT and DALL.E3 (a text-to-image generation model) to study place identity and found that both can capture the salient features of the city of interest. Others like Tao and Xu (2023) examined the capability of ChatGPT in different map making tasks (e.g., thematic and mental maps) based on either publicly available geographical data or conversation-based textual descriptions of geographic space. Similarly, Li and Ning (2023) introduced an autonomous GIS prototype by leveraging LLMs for tasks like geographical data collection, analysis, and visualization through natural language prompts. Some are calling this GeoQA (Geographic Question Answering, Feng et al., 2023) whereby researchers utilize LLMs to answer geographic questions in natural language. These studies, and others alike, align with the notion of "(n)ext generation of GIS: must be easy (Zhu et al., 2021)," emphasizing the shift towards more accessible and user-friendly GIS and spatial analysis technologies.What does this mean for urban analytics? Again, quoting from a past editorial by Batty (2019) the "term analytics implies a set of methods that can be used to explore, understand and predict properties and features of any system, in our case of cities." One example is the use of street view images to extract and understand the properties of the system or the space within a city. In the past, training and segmentation of such data was a time consumin...

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Spatial Analysis

Liu

2024

The Encyclopedia of Human Geography

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Autonomous GIS: the next-generation AI-powered GIS

Cited by 17 publications

References 20 publications

Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery

Exploration of an Open Vocabulary Model on Semantic Segmentation for Street Scene Imagery

Exploring the new frontier of information extraction through large language models in urban analytics

Spatial Analysis

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