Recently, for indoor personal drones, there have been a number of challenges in localizing users, such as how to accurately identify their location and identification. This paper proposes a distance measurement method for indoor Unmanned Aerial Vehicle (UAV) localization combining fine-time measurement (FTM) ranging equipment and the Deep Neural Network (DNN) model. Specifically, we can calculate the distance between the UAV and user by measuring the signal round-trip time. However, the indoor ranging accuracy of the FTM protocol is inevitably affected by the multipath and non-line-of-sight (NLOS). Besides, we prove that the FTM ranging under multipath is related to either the distance between the transceiver pair or the length of the reflected path relative to the direct one. Moreover, we also study the relationship between the FTM ranging error and the response of the multipath channel. Furthermore, we design an FTM error calibration model based on physical layer (PHY) information by using the DNN model, termed DeepF, which can not only automatically distinguish environmental characteristics but also estimate the length of propagation paths of a signal. The designed DeepF can adopt the DNN model to extract the time domain information of channel state information (CSI) and learn the nonlinear mapping between delay, power, and mean error from signal features. Finally, we use a trained model to calibrate the FTM error and predict the user location. Experimental results show DeepF significantly improves the ability of indoor UAVs to localize users.

Online Food Ordering Service (OFOS) is a popular location-based service that helps people order what they want. Compared with traditional e-commerce recommendation systems, users’ interests may be diverse under different spatiotemporal contexts, leading to various spatiotemporal data distributions, which increases the difficulty of model learning. However, numerous current works simply mix all samples to train a set of model parameters, which makes it challenging to capture the diversity in different spatiotemporal contexts. Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, further improving the fitting capability of the model. Specifically, a spatiotemporal-aware embedding layer performs weight adaptation on field granularity in feature embedding to achieve the purpose of dynamically perceiving spatiotemporal contexts. Meanwhile, we propose a spatiotemporal semantic transformation layer to explicitly convert the concatenated input of the raw semantic to the spatiotemporal semantic, which can further enhance the semantic representation under different spatiotemporal contexts. Furthermore, we introduce a novel spatiotemporal adaptive bias tower to capture diverse spatiotemporal bias, reducing the difficulty of modeling spatiotemporal distinction. To further verify the effectiveness of BASM, we propose two new metrics, Time-period-wise AUC (TAUC) and City-wise AUC (CAUC). Extensive offline evaluations on public and industrial datasets are conducted to demonstrate the effectiveness of our proposed model. The online A/B experiment also further illustrates the practicability of the model online service. This proposed method has now been implemented on Ele.me, a major online food ordering platform in China, serving more than 100 million online users.

The HJ-1 A/B satellite of China is widely used to monitor pollution, ecosystem destruction and natural disasters. Land surface temperature (LST) is required for a wide variety of scientific studies, from climatology to hydrology and ecology. In this paper, we propose Jimenez-Mufloz and Sobrino (JM&S) single-channel algorithm for retrieving LST from the infrared scanner (IRS) data of HJ-1B. Firstly, we got radiation intensity and brightness temperature from HJ-1B data. Then we calculated the atmospheric water vapor content with MODIS data. Afterwards, the emissivity was acquired with normalized difference vegetation index (NDVI). Finally, we retrieved LST with JM&S single-channel algorithm with these parameters. Through comparing LST retrieved from HJ-1B/IRS and MODIS LST product, we find that HJ-1B/IRS LST is basically consistent with the MODIS LST. Therefore, owing to high temporal resolution and large width, HJ-1B will play an important role on the research of LST retrieval.

The urban heat island (UHI) effect is becoming an influential factor on the city's environment along with the rapid development of urbanization process. This paper uses Landsat-5 TM RS image of the year 1988, 2007 and 2010 based on NDVITEMmodel and land temperature retrieval method to obtain Beijing land surface temperature distribution map through digital image processing and land surface temperature (LST) retrieval calculating using thermal infrared (TIR) remotely sensed satellite observation. The analysis showcases that urban heat island effect in Beijing has been dramatically intensified since 1988. Especially in recent years, the urban heat island effect has become increasingly apparent and seriously affected human's health in the way of changing temperature, barometric pressure, humidity, wind as well as air.