# People Detection Using Artificial Intelligence with Panchromatic Satellite Images
## 1 Introduction  
Currently, the detection of people and the quantification of human flows are mainly based on processing street camera imagery. However, due to their limited range of view, street cameras are not sufficient for monitoring large areas. Furthermore, the detection of people from satellite images proves to be more difficult than the detection of other target objects, such as planes or ships. Despite this challenge, satellite images promise many advantages, such as providing an overview of large areas within one time frame and monitoring closed or restricted areas as well as internal parts of residential or industrial complexes. People detection is of importance to security and defense, especially when tracking the movement and gathering of people in potentially dangerous areas. Economic and sociological studies evaluating the appearance of people in public spaces over a longer period of time can also be significant. Other uses include humanitarian monitoring, where the detection and counting of people in refugee camps or affected areas can improve the efficacy of humanitarian aid, border monitoring, and control of illegal immigration [1; 2; 3; 4; 5; 6; 7].  
The advent of new satellite missions and enhanced sensing capabilities exploration into this new area of applications. The spatial resolution of satellite imagery can reach as much as 30 cm, and new missions, especially satellite swarming and constellation, are becoming clear trends in Earth observation applications [8]. Single satellites simply cannot achieve the necessary frequency of images with the spatial or temporal resolution needed for analysis or provide the necessary data for decision making in near real time [9].  
The proliferation of high-resolution images and the development of machine learning (ML) algorithms have greatly enhanced the analysis of these data. Artificial intelligence has unlocked a new perspective on solving various problems, such as the detection of target objects, like people [10; 11]. Our study employs a convolutional neural network(CNN), which is a deep learning model capable of directly extracting features from satellite imagery data. It is effective at exploiting realistic imagery data characteristics and has been shown to be highly successful [7; 11]. Existing applications in remote satellite sensing are focused on the detection of large persistent objects, such as buildings or swimming pools [12]; applications for the detection of smaller objects, such as people, are lacking. When detecting people, various limitations exist, such as the resolution of satellite images, the costs of the images themselves, and the orientations of the images (bird's eye view).  
Our objective is to assess the current possibilities for people detection in an urban environment from satellite imagery with the highest available spatial resolution, and then to shed light on the potential future exploitation of satellite imagery for such applications. The biggest challenge to this end is the low spatial resolution of current satellite imagery, in which the overhead view of a person is reflected in only a few pixels (typically four pixels in our case). We propose using a combination of several techniques, such as Faster R-CNN, the extension of data augmentation by buffer use, and a specific validation process, to achieve the most accurate results.  
The paper is organized as follows: an introduction, a background that provides an overview of state-of-the-art CNN method technology, a description of the field of study and data sources, an explanation of CNN processing, the results and calculated quality parameters describing the success of the human detection methods, and, finally, a discussion of the results and limitations of these methods.