Current datasets for vehicular applications are mostly collected in North
America or Europe. Models trained or evaluated on these datasets might suffer
from geographical bias when deployed in other regions. Specifically, for scene
classification, a highway in a Latin American country differs drastically from
an Autobahn, for example, both in design and maintenance levels. We propose
VWise, a novel benchmark for road-type classification and scene classification
tasks, in addition to tasks focused on external contexts related to vehicular
applications in LatAm. We collected over 520 video clips covering diverse urban
and rural environments across Latin American countries, annotated with six
classes of road types. We also evaluated several state-of-the-art
classification models in baseline experiments, obtaining over 84% accuracy.
With this dataset, we aim to enhance research on vehicular tasks in Latin
America.

当前主要针对车辆应用的数据集大多数都是在北美或欧洲收集的，这对于在其他地区部署的训练或评估的模型可能会受到地理偏差的影响。本文提出了一个名为 VWise 的新型基准数据集，用于道路类型分类和场景分类任务，以及与拉丁美洲车辆应用有关的外部环境任务。我们收集了包含六类道路类型的 520 多个覆盖拉丁美洲各个城市和农村环境的视频片段，并在基准实验中评估了几个先进的分类模型，准确率达到了 84% 以上。通过这个数据集，我们旨在推动拉丁美洲车辆任务领域的研究。

VWise：用于评估车载应用场景分类的新型基准

VWise: A novel benchmark for evaluating scene classification for  vehicular applications

Data collection through censuses is conducted every 10 years on average in
Latin America, making it difficult to monitor the growth and support needed by
communities living in these settlements. Conducting a field survey requires
logistical resources to be able to do it exhaustively. The increasing
availability of open data, high-resolution satellite images, and free software
to process them allow us to be able to do so in a scalable way based on the
analysis of these sources of information. This case study shows the
collaboration between Dymaxion Labs and the NGO Techo to employ machine
learning techniques to create the first informal settlements census of
Tegucigalpa, Honduras.

该研究展示了 Dymaxion Labs 和 Techo NGO 之间的合作，利用机器学习技术通过高分辨率卫星图像创建洪都拉斯特古西格尔巴的首个非正式住区普查。

机器学习地图维护特古西加尔巴潜在非正规定居点进行地面勘测的案例研究

Case study: Mapping potential informal settlements areas in Tegucigalpa  with machine learning to plan ground survey

We describe the design, implementation, and evaluation of EMBERS, an
automated, 24x7 continuous system for forecasting civil unrest across 10
countries of Latin America using open source indicators such as tweets, news
sources, blogs, economic indicators, and other data sources. Unlike
retrospective studies, EMBERS has been making forecasts into the future since
Nov 2012 which have been (and continue to be) evaluated by an independent T&E
team (MITRE). Of note, EMBERS has successfully forecast the uptick and downtick
of incidents during the June 2013 protests in Brazil. We outline the system
architecture of EMBERS, individual models that leverage specific data sources,
and a fusion and suppression engine that supports trading off specific
evaluation criteria. EMBERS also provides an audit trail interface that enables
the investigation of why specific predictions were made along with the data
utilized for forecasting. Through numerous evaluations, we demonstrate the
superiority of EMBERS over baserate methods and its capability to forecast
significant societal happenings.

EMBERS 是一个 24x7 持续的自动化系统，使用 Twitter，新闻来源，博客，经济指标和其他数据源预测拉丁美洲 10 个国家的社会动荡，通过多次评估，我们证明了它的预测优于基准方法并有预测社会事件的能力。