Airport service quality evaluation is commonly found on social media,
including Google Maps. This valuable for airport management in order to enhance
the quality of services provided. However; prior studies either provide general
review for topics discussed by travellers or provide sentimental value to tag
the entire review without specifically mentioning the airport service that is
behind such value. Accordingly, this work proposes using aspect based
sentimental analysis in order to provide more detailed analysis for travellers
reviews. This works applied aspect based sentimental analysis on data collected
from Google Map about Dubai and Doha airports. The results provide tangible
reasons to use aspect based sentimental analysis in order to understand more
the travellers and spot airport services that are in need for improvement.

利用基于方面的情感分析从社交媒体和 Google 地图收集的数据对迪拜和多哈机场进行了评估，结果表明使用基于方面的情感分析可以更详细地分析旅行者的评论，以及发现需要改进的机场服务。

旅客评论的基于方面的情感分析

Aspect based sentimental analysis for travellers' reviews

If one sees the place name Houston Mercer Dog Run in New York, how does one
know how to pronounce it? Assuming one knows that Houston in New York is
pronounced "how-ston" and not like the Texas city, then one can probably guess
that "how-ston" is also used in the name of the dog park. We present a novel
architecture that learns to use the pronunciations of neighboring names in
order to guess the pronunciation of a given target feature. Applied to Japanese
place names, we demonstrate the utility of the model to finding and proposing
corrections for errors in Google Maps.
To demonstrate the utility of this approach to structurally similar problems,
we also report on an application to a totally different task: Cognate reflex
prediction in comparative historical linguistics. A version of the code has
been open-sourced
(this https URL).

该研究提出了一种新的架构，利用相邻位置名称的发音来推测目标位置名称的发音，并应用于日本地名，展示了该模型在查找和纠错谷歌地图中的错误方面的实用性，并将其应用于比较历史语言学中的同源反射预测。

有用的邻居：利用邻居提高地理要素的发音

Helpful Neighbors: Leveraging Neighbors in Geographic Feature Pronunciation

Up-to-date catalogs of the urban tree population are important for
municipalities to monitor and improve quality of life in cities. Despite much
research on automation of tree mapping, mainly relying on dedicated airborne
LiDAR or hyperspectral campaigns, trees are still mostly mapped manually in
practice. We present a fully automated tree detection and species recognition
pipeline to process thousands of trees within a few hours using publicly
available aerial and street view images of Google MapsTM. These data provide
rich information (viewpoints, scales) from global tree shapes to bark textures.
Our work-flow is built around a supervised classification that automatically
learns the most discriminative features from thousands of trees and
corresponding, public tree inventory data. In addition, we introduce a change
tracker to keep urban tree inventories up-to-date. Changes of individual trees
are recognized at city-scale by comparing street-level images of the same tree
location at two different times. Drawing on recent advances in computer vision
and machine learning, we apply convolutional neural networks (CNN) for all
classification tasks. We propose the following pipeline: download all available
panoramas and overhead images of an area of interest, detect trees per image
and combine multi-view detections in a probabilistic framework, adding prior
knowledge; recognize fine-grained species of detected trees. In a later,
separate module, track trees over time and identify the type of change. We
believe this is the first work to exploit publicly available image data for
fine-grained tree mapping at city-scale, respectively over many thousands of
trees. Experiments in the city of Pasadena, California, USA show that we can
detect > 70% of the street trees, assign correct species to > 80% for 40
different species, and correctly detect and classify changes in > 90% of the
cases.

本文分析了如何利用 Google 地图上的遥感图像进行城市规模的树木检测与物种识别，并提出了一种基于卷积神经网络的分类方法以及一种树木改变监测方法。通过实验证明该方法能够检测出超过 70% 的城市树木，正确地对超过 80% 的 40 种不同类型的树木物种进行分类，以及正确地检测并识别超过 90% 的变化操作。

从谷歌地图到精细化的街道树目录

From Google Maps to a Fine-Grained Catalog of Street trees

Landuse characterization is important for urban planning. It is traditionally
performed with field surveys or manual photo interpretation, two practices that
are time-consuming and labor-intensive. Therefore, we aim to automate landuse
mapping at the urban-object level with a deep learning approach based on data
from multiple sources (or modalities). We consider two image modalities:
overhead imagery from Google Maps and ensembles of ground-based pictures
(side-views) per urban-object from Google Street View (GSV). These modalities
bring complementary visual information pertaining to the urban-objects. We
propose an end-to-end trainable model, which uses OpenStreetMap annotations as
labels. The model can accommodate a variable number of GSV pictures for the
ground-based branch and can also function in the absence of ground pictures at
prediction time. We test the effectiveness of our model over the area of
\^Ile-de-France, France, and test its generalization abilities on a set of
urban-objects from the city of Nantes, France. Our proposed multimodal
Convolutional Neural Network achieves considerably higher accuracies than
methods that use a single image modality, making it suitable for automatic
landuse map updates. Additionally, our approach could be easily scaled to
multiple cities, because it is based on data sources available for many cities
worldwide.

本文介绍了一种利用多模态数据和深度学习方法的城市地块级别自动土地利用变更监测模型，并在法国区域和城市的测试中取得了很好的效果。