Inferring Nighttime Satellite Imagery from Human Mobility
- URL: http://arxiv.org/abs/2003.07691v1
- Date: Fri, 28 Feb 2020 14:25:11 GMT
- Title: Inferring Nighttime Satellite Imagery from Human Mobility
- Authors: Brian Dickinson, Gourab Ghoshal, Xerxes Dotiwalla, Adam Sadilek, Henry
Kautz
- Abstract summary: Nighttime lights satellite imagery has been used for decades as a uniform, global source of data for studying a wide range of socioeconomic factors.
Recently, another more terrestrial source is producing data with similarly uniform global coverage: anonymous and aggregated smart phone location.
This data, which measures the movement patterns of people and populations rather than the light they produce, could prove just as valuable in decades to come.
- Score: 3.206489953505937
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Nighttime lights satellite imagery has been used for decades as a uniform,
global source of data for studying a wide range of socioeconomic factors.
Recently, another more terrestrial source is producing data with similarly
uniform global coverage: anonymous and aggregated smart phone location. This
data, which measures the movement patterns of people and populations rather
than the light they produce, could prove just as valuable in decades to come.
In fact, since human mobility is far more directly related to the socioeconomic
variables being predicted, it has an even greater potential. Additionally,
since cell phone locations can be aggregated in real time while preserving
individual user privacy, it will be possible to conduct studies that would
previously have been impossible because they require data from the present. Of
course, it will take quite some time to establish the new techniques necessary
to apply human mobility data to problems traditionally studied with satellite
imagery and to conceptualize and develop new real time applications. In this
study we demonstrate that it is possible to accelerate this process by
inferring artificial nighttime satellite imagery from human mobility data,
while maintaining a strong differential privacy guarantee. We also show that
these artificial maps can be used to infer socioeconomic variables, often with
greater accuracy than using actual satellite imagery. Along the way, we find
that the relationship between mobility and light emissions is both nonlinear
and varies considerably around the globe. Finally, we show that models based on
human mobility can significantly improve our understanding of society at a
global scale.
Related papers
- Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
We develop a novel generative deep learning approach for human mobility modeling and synthesis.
It incorporates both activity patterns and location trajectories using open-source data.
The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
arXiv Detail & Related papers (2024-05-24T02:04:10Z) - Event detection from novel data sources: Leveraging satellite imagery
alongside GPS traces [0.9075220953694432]
We propose a novel data fusion methodology integrating satellite imagery with privacy-enhanced mobile data to augment the event inference task.
The expected use cases for our methodology include small-scale disaster detection (i.e., tornadoes, wildfires, and floods) in rural regions, search and rescue operation augmentation for lost hikers in remote wilderness areas, and identification of active conflict areas and population displacement in war-torn states.
arXiv Detail & Related papers (2024-01-19T18:59:37Z) - Social-Transmotion: Promptable Human Trajectory Prediction [65.80068316170613]
Social-Transmotion is a generic Transformer-based model that exploits diverse and numerous visual cues to predict human behavior.
Our approach is validated on multiple datasets, including JTA, JRDB, Pedestrians and Cyclists in Road Traffic, and ETH-UCY.
arXiv Detail & Related papers (2023-12-26T18:56:49Z) - On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
We propose a socioeconomic-aware deep model called DeepSEI.
The DeepSEI model incorporates two networks called deep network and recurrent network.
We conduct extensive experiments on real mobility records data, POI data and house prices data.
arXiv Detail & Related papers (2022-11-15T15:07:45Z) - Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep
Learning [77.34726150561087]
We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data.
We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
arXiv Detail & Related papers (2022-09-28T18:51:59Z) - GIMO: Gaze-Informed Human Motion Prediction in Context [75.52839760700833]
We propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, and ego-centric views with eye gaze.
Our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects.
To realize the full potential of gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches.
arXiv Detail & Related papers (2022-04-20T13:17:39Z) - Generating synthetic mobility data for a realistic population with RNNs
to improve utility and privacy [3.3918638314432936]
We present a system for generating synthetic mobility data using a deep recurrent neural network (RNN)
The system takes a population distribution as input and generates mobility traces for a corresponding synthetic population.
We show the generated mobility data retain the characteristics of the real data, while varying from the real data at the individual level.
arXiv Detail & Related papers (2022-01-04T13:58:22Z) - HSPACE: Synthetic Parametric Humans Animated in Complex Environments [67.8628917474705]
We build a large-scale photo-realistic dataset, Human-SPACE, of animated humans placed in complex indoor and outdoor environments.
We combine a hundred diverse individuals of varying ages, gender, proportions, and ethnicity, with hundreds of motions and scenes, in order to generate an initial dataset of over 1 million frames.
Assets are generated automatically, at scale, and are compatible with existing real time rendering and game engines.
arXiv Detail & Related papers (2021-12-23T22:27:55Z) - Biases in human mobility data impact epidemic modeling [0.0]
We identify two types of bias caused by unequal access to, and unequal usage of mobile phones.
We find evidence for data generation bias in all examined datasets in that high-wealth individuals are overrepresented.
To mitigate the skew, we present a framework to debias data and show how simple techniques can be used to increase representativeness.
arXiv Detail & Related papers (2021-12-23T13:20:54Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - Understanding individual behaviour: from virtual to physical patterns [5.991701520084448]
We analyse and discuss the mobility and application usage of 400,000 individuals over eight months.
We find an astonishing similarity between people's mobility in the physical space and how they move from app to app in smartphones.
We see these findings as crucial to enrich a discussion for the potentials and the challenges of building human-centric AI systems.
arXiv Detail & Related papers (2020-02-13T14:04:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.