IndiaScene365: a transfer learning dataset for Indian scene understanding in diverse weather condition
Year: 2026
Type: article
Abstract: detail [15]. This limits the ability to train models 111 that can generalize well to such conditions. Foggy 112 Zurich [3] and Foggy Cityscapes [4] introduced 113 synthetic fog images by applying masks to original 114 images. Synthetic rain datasets [14] include 115 Rain1400, RainyCityscapes Rain100H, and 116 Rain12. Datasets like, BDD100K, ACDC [11], and 117 IDD [1], contain authentic images collected from 118 various adverse conditions [15], including fog, 119 rain, snow, and nighttime scenarios [5] [6]. The label set used in dataset preparation is identical to that in IDD [1] The hierarchy in the category of class labels adds a higher degree of complexity [5] to our dataset compared to existing datasets like Cityscapes [7], and even when compared to adverse weather datasets such as Foggy Cityscapes [8]
Authors: Deepa Mane, Sandhya Arora, Sachin Shelke
Institution: Savitribai Phule Pune University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W7119502455