# Geographical Information System Enhancement Using Active Learning-Enhanced Semantic Segmentation
## 1 Introduction  
Drones, or Unmanned Aerial Vehicles (UAVs), have transformed the landscape of aerial photography. Their ability to operate at lower altitudes, coupled with their ability to capture high-resolution imagery, presents a cost-effective alternative to traditional methods. This has led researchers and businesses alike to adopt drones for various applications. Given their adaptability, drones can operate both indoors and outdoors, and the integration of diverse camera systems allows them to navigate around obstacles with precision. Today, drones are widely used in surveillance, agriculture, forestry, wildlife monitoring, disaster management, and even facial recognition [1; 2]. Their surge in popularity can be attributed to their potential role in fostering sustainable urban environments [3]. Globally, UAVs are being recognized as potent tools for remote sensing in agricultural and environmental sectors [4; 5]. One of the emerging areas is the use of remote sensing data for tree identification and mapping, which is pivotal for forest management [6]. In the construction realm, drones are game changers. Their capabilities range from pre-construction surveys, ensuring detailed mapping of site topography and underground infrastructures, to real-time monitoring of construction activities. Moreover, drones are making high-altitude inspections safer, obviating the need for risk-prone traditional methods. By enabling precise surveying, drones are reducing both the duration and costs associated with construction processes [7; 8; 9]. Semantic segmentation, an advanced computer vision technique, enhances the efficacy of image analysis, especially for images captured by drones. This technique classifies image pixels into coherent segments or objects, finding applications across construction, agriculture, and disaster management domains [10; 11]. Incorporating drone-captured images into building information systems is of paramount importance. Traditional aerial or satellite imagery, often plagued by lower resolution and outdated data, is being outpaced by drone imagery, making it the go-to solution for updating building information systems. In the realm of semantic segmentation, algorithms like SegNet, DeepLab, and U-Net, which are derivatives of convolutional neural networks (CNNs), have been prominent. These models, with their foundation in CNNs, are adept at image classification tasks. For instance, U-Net employs an output feature map as its input, enabling it to classify intricate images more accurately. SegNet, tailored for image segmentation, enhances CNNs by preserving intricate information through decoding prior pooling layers [12]. DeepLab, an evolved version of SegNet, excels in processing high-resolution imagery, ensuring the clear classification of even the most complex images [13; 14; 15]. In this research, while our focus was on detecting roofs using semantic segmentation, deliberate choices were made not to use other discriminative techniques like edge detection. Our primary objective was to concentrate on the most suitable semantic segmentation technique using human-labeled training data. This study underscores the significance of labeling and refining data, leveraging only the building information registered in the GIS (geographic information system). Our approach leans towards active learning, wherein automatically labeled data undergo refinement with minimal human intervention, resulting in a curated labeled training dataset [16]. Often, when delineating specific areas in satellite or drone images, the parcel data from the geographic information system is harnessed, considering any discrepancies or errors [17; 18]. Manual labeling, while detailed, is labor-intensive and prone to errors. This necessitates the optimal use of GIS data. At times, discrepancies in images may arise due to inaccuracies in GIS building data or challenges in deciphering image data. In this study, the aim was to enhance the training dataset by gauging network similarity during the refinement phase.