Improving Road Surface Area Extraction via Semantic Segmentation with Conditional Generative Learning for Deep Inpainting Operations

The road surface area extraction task is generally carried out via semantic segmentation over remotely-sensed imagery. However, this supervised learning task is often costly as it requires remote sensing images labelled at the pixel level, and the results are not always satisfactory (presence of discontinuities, overlooked connection points, or isolated road segments). On the other hand, unsupervised learning does not require labelled data and can be employed for post-processing the geometries of geospatial objects extracted via semantic segmentation. In this work, we implement a conditional Generative Adversarial Network to reconstruct road geometries via deep inpainting procedures on a new dataset containing unlabelled road samples from challenging areas present in official cartographic support from Spain. The goal is to improve the initial road representations obtained with semantic segmentation models via generative learning. The performance of the model was evaluated on unseen data by conducting a metrical comparison where a maximum Intersection over Union (IoU) score improvement of 1.3% was observed when compared to the initial semantic segmentation result. Next, we evaluated the appropriateness of applying unsupervised generative learning using a qualitative perceptual validation to identify the strengths and weaknesses of the proposed method in very complex scenarios and gain a better intuition of the model’s behaviour when performing large-scale post-processing with generative learning and deep inpainting procedures and observed important improvements in the generated data.

Feasibility of Physics Teaching Materials With Qur'an Using Generative Learning Model on Sound Waves

Preliminary observations show the limitations of teaching materials that have a relationship with spiritual aspects, especially the Qur'an, which is due to the limitations of teaching materials that contain these aspects. Required learning that stimulates students to generalize religious aspects and learn physics. Therefore, the purpose of this study is to describe the feasibility of physics teaching materials containing the Qur'an by using a generative model on sound wave material. This study aims to describe the validity, practicality, and effectiveness of the teaching materials developed. The research was conducted on 33 students at class XI MIPA 1 MAN 2 Model Banjarmasin using the ADDIE model. Data was taken from the validation results of teaching materials using validation sheet, lesson plan implementation using lesson plan implementation sheet, and students' learning outcomes at the pretest and post-test using learning outcomes tests. The results of this study were 1) the validity of the teaching materials was in the "valid" category with a value of 3.33, 2) the practicality of the teaching materials was in the "very practical" category with a value of 3.63, and 3) the effectiveness of the teaching material was in the "effective/moderate" category with a value of 0,44. This study concluded that physics teaching materials containing Al-Qur'an verses on sound wave materials using generative learning models are declared feasible to be used based on the validity, practicality, and effectiveness of the teaching materials. The implication of this research is the creation of teaching materials containing the Qur'an that are used in schools and creating student habits to link physics and the Qur'an.