# Evaluation of Convolutional Neural Network For Image Classification with Agricultural and Urban Datasets

arXiv: https://arxiv.org/abs/2601.01393 · HTML: https://mlsanity.com/paper/2601.01393/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 17 references that are in the map.
- First posted on arXiv 4 Jan 2026.

## Builds on (most cited first, 4 of 4)

- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Road Damage and Manhole Detection using Deep Learning for Smart Cities: A Polygonal Annotation Approach](https://mlsanity.com/paper/2510.03797/) (2025)
- [Precision-Recall Curve (PRC) Classification Trees](https://mlsanity.com/paper/2011.07640/) (2020)
- [Detecting Unauthorized Vehicles using Deep Learning for Smart Cities: A Case Study on Bangladesh](https://mlsanity.com/paper/2510.26154/) (2025)

## Abstract (arXiv)

This paper presents the development and evaluation of a custom Convolutional Neural Network (CustomCNN) created to study how architectural design choices affect multi-domain image classification tasks. The network uses residual connections, Squeeze-and-Excitation attention mechanisms, progressive channel scaling, and Kaiming initialization to improve its ability to represent data and speed up training. The model is trained and tested on five publicly available datasets: unauthorized vehicle detection, footpath encroachment detection, polygon-annotated road damage and manhole detection, MangoImageBD and PaddyVarietyBD. A comparison with popular CNN architectures shows that the CustomCNN delivers competitive performance while remaining efficient in computation. The results underscore the importance of thoughtful architectural design for real-world Smart City and agricultural imaging applications.