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

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]

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Publication Details

Journal
Frontiers in Artificial Intelligence
Published
2026-01-09
DOI
https://doi.org/10.3389/frai.2025.1669512
Primary Topic
Image Enhancement Techniques
Type
article
Field-Weighted Citation Impact
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article

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

Sachin D. Shelke, Sandhya Arora, Deepa Mane
Frontiers in Artificial Intelligence
Image Enhancement Techniques
article

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

Sachin D. Shelke, Sandhya Arora, Deepa Mane
article en

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]

Frontiers in Artificial IntelligenceVol. 8
Indian Institute of Information Technology, Pune (IN), Smt. Kashibai Navale Medical College and General hospital (IN), Savitribai Phule Pune University (IN)
Sustainable cities and communities
Openalex Percentile: Top 94%
Image Enhancement Techniques
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