# Land-Cover Classification Using Deep Learning with High-Resolution Remote-Sensing Imagery
## 5 Conclusions  
In this study, we introduced a robust approach to land-area scene classification (LASC) through the implementation of three distinct deep learning (DL) models--Inception-v3, DenseNet121, and ResNet-50--leveraging the power of transfer learning. Our LASC system demonstrates impressive accuracy rates of 92%, 91%, and 91% in classifying both static and real-time land-area scenes from images. While these results show promise, there remains potential for further enhancement. Our system has the capacity to evolve, extending its capabilities to classify multiple land-area scene categories in real time. Such advancements could significantly contribute to improved urban planning and land management across both urban and rural areas, benefiting a diverse range of stakeholders. Upon analyzing our models' performance metrics, including _recall_, _accuracy_, _precision_, and _F1-score_, Inception-v3 emerges as the top performer, outclassing DenseNet121 and ResNet-50. Inception-v3 excels in accurately classifying land-area scene poses in images, achieving remarkable scores of 92% _accuracy_, 93% _precision_, 92% _recall_, and 92% _F1-score_. DenseNet121 follows closely with scores of 91%, 91%, 90%, and 89%, while the ResNet-50 model achieves scores of 91%, 91%, 90%, and 88%, respectively.  
Our future endeavors with the LASC system will focus on the development of robotics and smartphone applications. Additionally, we aim to enhance the system's accuracy by creating an improved CNN incorporating advanced data fusion techniques. This strategic approach is expected to result in even more precise land-area scene classification, ultimately facilitating more informed decision-making in the realms of land management and urban planning. The widespread applications of these advancements underscore the potential benefits for various stakeholders in diverse domains.  
Conceptualization, M.F. and J.N.; methodology, M.F. and J.N.; software, M.F. and J.N.; validation, M.F., J.N. and L.M.D.; formal analysis, L.M.D.; investigation, H.M.; resources, H.M.; data curation M.F. and J.N.; writing--original draft preparation, M.F. and J.N.; writing--review and editing, L.M.D.; visualization, M.F., J.N. and H.-K.S.; supervision, H.-K.S.; project administration, H.M.; funding acquisition, H.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2020R1A6A1A03038540); the Institute of Information and Communications Technology Planning and Evaluation (IITP) under the Metaverse Support Program to Nurture the Best Talents (IITP-2023-RS-2023-00254529) grant funded by the Korean government (MSIT); and the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through the Digital Breeding Transformation Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322063-03-1-SB010).  
Not applicable.  
Data Availability Statement:Data is contained within the article.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **UC Merced** & **AID** \\ \hline No. of Classes & 18 & 30 \\ \hline No. of images & 18,000 & 9000 \\ \hline Inception V3 Acc & 92\% & 86\% \\ \hline DenseNet121 & 91\% & 89\% \\ \hline Res-Net50 & 91\% & 85\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative analysis of datasets.