  The advent of large language models (LLMs) has heightened interest in their
potential for multimodal applications that integrate language and vision. This
paper explores the capabilities of GPT-4V in the realms of geography,
environmental science, agriculture, and urban planning by evaluating its
performance across a variety of tasks. Data sources comprise satellite imagery,
aerial photos, ground-level images, field images, and public datasets. The
model is evaluated on a series of tasks including geo-localization, textual
data extraction from maps, remote sensing image classification, visual question
answering, crop type identification, disease/pest/weed recognition, chicken
behavior analysis, agricultural object counting, urban planning knowledge
question answering, and plan generation. The results indicate the potential of
GPT-4V in geo-localization, land cover classification, visual question
answering, and basic image understanding. However, there are limitations in
several tasks requiring fine-grained recognition and precise counting. While
zero-shot learning shows promise, performance varies across problem domains and
image complexities. The work provides novel insights into GPT-4V's capabilities
and limitations for real-world geospatial, environmental, agricultural, and
urban planning challenges. Further research should focus on augmenting the
model's knowledge and reasoning for specialized domains through expanded
training. Overall, the analysis demonstrates foundational multimodal
intelligence, highlighting the potential of multimodal foundation models (FMs)
to advance interdisciplinary applications at the nexus of computer vision and
language.
