IndiaScene365: a transfer learning dataset for Indian scene understanding in diverse weather condition

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Authors: Deepa Mane, Sandhya Arora, Sachin Shelke
Cited by: 0
References: 15

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]

Fields of Study & Subjects

Image Enhancement Techniques Advanced Neural Network Applications Computer Graphics and Visualization Techniques

Publication details

Journal / source
Frontiers in Artificial Intelligence
Volume
8
Pages
1669512–1669512
Publication date
9 January 2026
Language
EN