Understanding urban dynamics and promoting sustainable development requires
comprehensive insights about buildings. While geospatial artificial
intelligence has advanced the extraction of such details from Earth
observational data, existing methods often suffer from computational
inefficiencies and inconsistencies when compiling unified building-related
datasets for practical applications. To bridge this gap, we introduce the
Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for
simultaneous extraction of spatial and attributional building details from
high-resolution satellite imagery, exemplified by building rooftops, urban
functional types, and roof architectural types. Notably, MT-BR can be
fine-tuned to incorporate additional building details, extending its
applicability. For large-scale applications, we devise a novel spatial sampling
scheme that strategically selects limited but representative image samples.
This process optimizes both the spatial distribution of samples and the urban
environmental characteristics they contain, thus enhancing extraction
effectiveness while curtailing data preparation expenditures. We further
enhance MT-BR's predictive performance and generalization capabilities through
the integration of advanced augmentation techniques. Our quantitative results
highlight the efficacy of the proposed methods. Specifically, networks trained
with datasets curated via our sampling method demonstrate improved predictive
accuracy relative to those using alternative sampling approaches, with no
alterations to network architecture. Moreover, MT-BR consistently outperforms
other state-of-the-art methods in extracting building details across various
metrics. The real-world practicality is also demonstrated in an application
across Shanghai, generating a unified dataset that encompasses both the spatial
and attributional details of buildings.

为了理解城市动态并促进可持续发展，我们引入了适用于从高分辨率卫星图像中同时提取空间和属性建筑细节的多任务建筑细化器（MT-BR），通过我们的采样方法训练的网络在预测准确性方面相对其他采样方法表现出改进，而 MT-BR 则在提取建筑细节方面始终优于其他先进方法，将在上海的应用中展示其实际可行性。

多任务深度学习用于高分辨率卫星图像中的大规模建筑细节提取

Multi-task deep learning for large-scale building detail extraction from  high-resolution satellite imagery

We consider the problem of active learning in the context of spatial sampling
for level set estimation (LSE), where the goal is to localize all regions where
a function of interest lies above/below a given threshold as quickly as
possible. We present a finite-horizon search procedure to perform LSE in one
dimension while optimally balancing both the final estimation error and the
distance traveled for a fixed number of samples. A tuning parameter is used to
trade off between the estimation accuracy and distance traveled. We show that
the resulting optimization problem can be solved in closed form and that the
resulting policy generalizes existing approaches to this problem. We then show
how this approach can be used to perform level set estimation in higher
dimensions under the popular Gaussian process model. Empirical results on
synthetic data indicate that as the cost of travel increases, our method's
ability to treat distance nonmyopically allows it to significantly improve on
the state of the art. On real air quality data, our approach achieves roughly
one fifth the estimation error at less than half the cost of competing
algorithms.

我们考虑在空间采样的背景下的主动学习问题，目标是在尽快的情况下定位所有感兴趣函数超过 / 低于给定阈值的区域。我们提出了一个有限时间搜索过程，以在一维空间中执行级别集估计，同时在固定样本数量的情况下最佳地平衡估计误差和行进距离。我们使用调节参数来权衡估计精度和行进距离。我们展示了该优化问题可以以闭合形式求解，并且该策略可以推广到该问题的现有方法。然后我们展示了如何在流行的高斯过程模型下执行高维级别集估计。对于合成数据的实证结果表明，随着旅行成本的增加，我们的方法通过非迈逐地对待距离，显著改进了现有技术水平。在真实的空气质量数据上，我们的方法在大约一半的开销下实现了大约五分之一的估计误差。

主动水平集估计的有限时间方法

A Finite-Horizon Approach to Active Level Set Estimation

We introduce a saliency-based distortion layer for convolutional neural
networks that helps to improve the spatial sampling of input data for a given
task. Our differentiable layer can be added as a preprocessing block to
existing task networks and trained altogether in an end-to-end fashion. The
effect of the layer is to efficiently estimate how to sample from the original
data in order to boost task performance. For example, for an image
classification task in which the original data might range in size up to
several megapixels, but where the desired input images to the task network are
much smaller, our layer learns how best to sample from the underlying high
resolution data in a manner which preserves task-relevant information better
than uniform downsampling. This has the effect of creating distorted,
caricature-like intermediate images, in which idiosyncratic elements of the
image that improve task performance are zoomed and exaggerated. Unlike
alternative approaches such as spatial transformer networks, our proposed layer
is inspired by image saliency, computed efficiently from uniformly downsampled
data, and degrades gracefully to a uniform sampling strategy under uncertainty.
We apply our layer to improve existing networks for the tasks of human gaze
estimation and fine-grained object classification. Code for our method is
available in: this http URL

本文提出了一种基于显著性的畸变层，通过对输入数据进行空间采样来提高给定任务的性能表现，并成功运用于眼球追踪和细粒度物体分类等任务中。