A Comprehensive Indoor Environment Dataset from Single-family Houses in the US
The paper describes a dataset comprising indoor environmental factors such as temperature, humidity, air quality, and noise levels. The data was collected from 10 sensing devices installed in various locations within three single-family houses in Virginia, USA. The objective of the data collection was to study the indoor environmental conditions of the houses over time. The data were collected at a frequency of one record per minute for a year, combining over 2.5 million records. The paper provides actual floor plans with sensor placements to aid researchers and practitioners in creating reliable building performance models. The techniques used to collect and verify the data are also explained in the paper. The resulting dataset can be employed to enhance models for building energy consumption, occupant behavior, predictive maintenance, and other relevant purposes.
Code (1)
Similar Papers 제목 키워드 기반
A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors
This paper presents a comprehensive dataset of LoRaWAN technology path loss measurements collected in an indoor office environment, focusing on quantifying the effects of environmental factors on signal propagation. Util…
A Benchmark Dataset for Collaborative SLAM in Service Environments
As service environments have become diverse, they have started to demand complicated tasks that are difficult for a single robot to complete. This change has led to an interest in multiple robots instead of a single robo…
INDOOR-LiDAR: Bridging Simulation and Reality for Robot-Centric 360 degree Indoor LiDAR Perception -- A Robot-Centric Hybrid Dataset
We present INDOOR-LIDAR, a comprehensive hybrid dataset of indoor 3D LiDAR point clouds designed to advance research in robot perception. Existing indoor LiDAR datasets often suffer from limited scale, inconsistent annot…
Scene Understanding3D Object DetectionDomain AdaptationPoint CloudsEfficient Multi-Task RGB-D Scene Analysis for Indoor Environments
Semantic scene understanding is essential for mobile agents acting in various environments. Although semantic segmentation already provides a lot of information, details about individual objects as well as the general sc…
Instance SegmentationPanoptic SegmentationScene ClassificationScene Classification (unified classes)+3SITUATE: Indoor Human Trajectory Prediction through Geometric Features and Self-Supervised Vision Representation
Patterns of human motion in outdoor and indoor environments are substantially different due to the scope of the environment and the typical intentions of people therein. While outdoor trajectory forecasting has received …
Trajectory ForecastingTrajectory Prediction