A building's age of construction is crucial for supporting many geospatial
applications. Much current research focuses on estimating building age from
facade images using deep learning. However, building an accurate deep learning
model requires a considerable amount of labelled training data, and the trained
models often have geographical constraints. Recently, large pre-trained vision
language models (VLMs) such as GPT-4 Vision, which demonstrate significant
generalisation capabilities, have emerged as potential training-free tools for
dealing with specific vision tasks, but their applicability and reliability for
building information remain unexplored. In this study, a zero-shot building age
classifier for facade images is developed using prompts that include logical
instructions. Taking London as a test case, we introduce a new dataset,
FI-London, comprising facade images and building age epochs. Although the
training-free classifier achieved a modest accuracy of 39.69%, the mean
absolute error of 0.85 decades indicates that the model can predict building
age epochs successfully albeit with a small bias. The ensuing discussion
reveals that the classifier struggles to predict the age of very old buildings
and is challenged by fine-grained predictions within 2 decades. Overall, the
classifier utilising GPT-4 Vision is capable of predicting the rough age epoch
of a building from a single facade image without any training.

使用预训练视觉语言模型 GPT-4 Vision，开发了一个零训练的建筑物立面图像年龄分类器，能够从单一立面图像中粗略预测建筑物的年代。

利用 GPT-4 从建筑 fasade 图像进行零样本建筑年龄分类

Zero-shot Building Age Classification from Facade Image Using GPT-4

Information on the depth of floodwater is crucial for rapid mapping of areas
affected by floods. However, previous approaches for estimating floodwater
depth, including field surveys, remote sensing, and machine learning
techniques, can be time-consuming and resource-intensive. This paper presents
an automated and fast approach for estimating floodwater depth from on-site
flood photos. A pre-trained large multimodal model, GPT-4 Vision, was used
specifically for estimating floodwater. The input data were flooding photos
that contained referenced objects, such as street signs, cars, people, and
buildings. Using the heights of the common objects as references, the model
returned the floodwater depth as the output. Results show that the proposed
approach can rapidly provide a consistent and reliable estimation of floodwater
depth from flood photos. Such rapid estimation is transformative in flood
inundation mapping and assessing the severity of the flood in near-real time,
which is essential for effective flood response strategies.

用于估计洪水深度的自动化快速方法，利用洪水照片和 GPT-4 Vision 模型，通过参考物体的高度返回洪水深度估计结果。该方法能够快速、准确地评估洪水深度，对洪水内涝图绘制和洪水严重程度的评估具有重要意义。

利用大规模多模型自动估计洪水深度进行快速洪水制图

Automated Floodwater Depth Estimation Using Large Multimodal Model for  Rapid Flood Mapping

In this study, we tackle the challenge of classifying the object category in
point clouds, which previous works like PointCLIP struggle to address due to
the inherent limitations of the CLIP architecture. Our approach leverages GPT-4
Vision (GPT-4V) to overcome these challenges by employing its advanced
generative abilities, enabling a more adaptive and robust classification
process. We adapt the application of GPT-4V to process complex 3D data,
enabling it to achieve zero-shot recognition capabilities without altering the
underlying model architecture. Our methodology also includes a systematic
strategy for point cloud image visualization, mitigating domain gap and
enhancing GPT-4V's efficiency. Experimental validation demonstrates our
approach's superiority in diverse scenarios, setting a new benchmark in
zero-shot point cloud classification.

我们研究了在点云中分类物体类别的挑战，通过使用 GPT-4 Vision（GPT-4V）的先进生成能力，我们的方法能够处理复杂的 3D 数据，在不改变底层模型架构的情况下实现了零样本识别能力，并通过系统策略进行点云图像可视化，提高了 GPT-4V 的效率。实验证明了我们方法在各种情景下的优越性，设定了零样本点云分类的新基准。

利用 GPT-4 视觉进行零样本点云理解

Exploiting GPT-4 Vision for Zero-shot Point Cloud Understanding

This technical report delves into the application of GPT-4 Vision (GPT-4V) in
the nuanced realm of COVID-19 image classification, leveraging the
transformative potential of in-context learning to enhance diagnostic
processes.

本报告介绍了 GPT-4 Vision（GPT-4V）在 COVID-19 图像分类领域的应用，利用上下文学习的变革潜力增强诊断过程。