We perform fine-grained land use mapping at the city scale using ground-level
images. Mapping land use is considerably more difficult than mapping land cover
and is generally not possible using overhead imagery as it requires close-up
views and seeing inside buildings. We postulate that the growing collections of
georeferenced, ground-level images suggest an alternate approach to this
geographic knowledge discovery problem. We develop a general framework that
uses Flickr images to map 45 different land-use classes for the City of San
Francisco. Individual images are classified using a novel convolutional neural
network containing two streams, one for recognizing objects and another for
recognizing scenes. This network is trained in an end-to-end manner directly on
the labeled training images. We propose several strategies to overcome the
noisiness of our user-generated data including search-based training set
augmentation and online adaptive training. We derive a ground truth map of San
Francisco in order to evaluate our method. We demonstrate the effectiveness of
our approach through geo-visualization and quantitative analysis. Our framework
achieves over 29% recall at the individual land parcel level which represents a
strong baseline for the challenging 45-way land use classification problem
especially given the noisiness of the image data.

利用地面图像进行城市尺度上的精细土地利用分类，使用地理引用的 Flickr 图像创建框架并使用卷积神经网络进行分类。

利用地面图像进行城市尺度的细粒度土地利用分类

Fine-Grained Land Use Classification at the City Scale Using  Ground-Level Images

We propose a novel convolutional neural network architecture for estimating
geospatial functions such as population density, land cover, or land use. In
our approach, we combine overhead and ground-level images in an end-to-end
trainable neural network, which uses kernel regression and density estimation
to convert features extracted from the ground-level images into a dense feature
map. The output of this network is a dense estimate of the geospatial function
in the form of a pixel-level labeling of the overhead image. To evaluate our
approach, we created a large dataset of overhead and ground-level images from a
major urban area with three sets of labels: land use, building function, and
building age. We find that our approach is more accurate for all tasks, in some
cases dramatically so.

本文提出了一种新型卷积神经网络架构，用于估计地理空间函数，并使用各种标签创建了一个大型数据集进行评估。

近距离和远程感知的统一模型

A Unified Model for Near and Remote Sensing

Land use mapping is a fundamental yet challenging task in geographic science.
In contrast to land cover mapping, it is generally not possible using overhead
imagery. The recent, explosive growth of online geo-referenced photo
collections suggests an alternate approach to geographic knowledge discovery.
In this work, we present a general framework that uses ground-level images from
Flickr for land use mapping. Our approach benefits from several novel aspects.
First, we address the nosiness of the online photo collections, such as
imprecise geolocation and uneven spatial distribution, by performing location
and indoor/outdoor filtering, and semi- supervised dataset augmentation. Our
indoor/outdoor classifier achieves state-of-the-art performance on several
bench- mark datasets and approaches human-level accuracy. Second, we utilize
high-level semantic image features extracted using deep learning, specifically
convolutional neural net- works, which allow us to achieve upwards of 76%
accuracy on a challenging eight class land use mapping problem.

使用 Flickr 上的地面图像和深度学习方法进行土地利用地图绘制，并针对地理位置和室内 / 室外进行筛选和半监督数据增强，从而最终实现了 76% 的准确性。

应用卷积神经网络对地面图像进行土地利用分类

Land Use Classification using Convolutional Neural Networks Applied to  Ground-Level Images

We propose to use deep convolutional neural networks to address the problem
of cross-view image geolocalization, in which the geolocation of a ground-level
query image is estimated by matching to georeferenced aerial images. We use
state-of-the-art feature representations for ground-level images and introduce
a cross-view training approach for learning a joint semantic feature
representation for aerial images. We also propose a network architecture that
fuses features extracted from aerial images at multiple spatial scales. To
support training these networks, we introduce a massive database that contains
pairs of aerial and ground-level images from across the United States. Our
methods significantly out-perform the state of the art on two benchmark
datasets. We also show, qualitatively, that the proposed feature
representations are discriminative at both local and continental spatial
scales.

本文旨在使用卷积神经网络来解决跨视角图像地理定位的问题，并介绍了用于地面图像和航拍图像的特征表示，并提出一种融合多个空间尺度提取的特征的网络架构，并通过实验表明其比现有方法更为优越。