Excited to share our paper which introduces self-supervised learning into the field of parking spot occupancy recognition.</p>\n","updatedAt":"2026-06-23T14:14:50.537Z","author":{"_id":"672a1eadae82a4f934afca64","avatarUrl":"/avatars/ca8d68a70a897561717b986c17f1e120.svg","fullname":"Luan Marko Kujavski","name":"LoanMaikon","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.9605661034584045},"editors":["LoanMaikon"],"editorAvatarUrls":["/avatars/ca8d68a70a897561717b986c17f1e120.svg"],"reactions":[],"isReport":false}}],"primaryEmailConfirmed":false,"paper":{"id":"2606.20886","authors":[{"_id":"6a3a7cebfdcd3514343bb858","user":{"_id":"672a1eadae82a4f934afca64","avatarUrl":"/avatars/ca8d68a70a897561717b986c17f1e120.svg","isPro":false,"fullname":"Luan Marko Kujavski","user":"LoanMaikon","type":"user","name":"LoanMaikon"},"name":"Luan Marko Kujavski","status":"claimed_verified","statusLastChangedAt":"2026-06-23T13:55:25.580Z","hidden":false},{"_id":"6a3a7cebfdcd3514343bb859","name":"Rayson Laroca","hidden":false},{"_id":"6a3a7cebfdcd3514343bb85a","name":"Paulo Lisboa de Almeida","hidden":false}],"mediaUrls":["https://cdn-uploads.huggingface.co/production/uploads/672a1eadae82a4f934afca64/zF4V2JXAmNmvADqjUR5qp.jpeg"],"publishedAt":"2026-06-18T00:00:00.000Z","submittedOnDailyAt":"2026-06-23T00:00:00.000Z","title":"Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach","submittedOnDailyBy":{"_id":"672a1eadae82a4f934afca64","avatarUrl":"/avatars/ca8d68a70a897561717b986c17f1e120.svg","isPro":false,"fullname":"Luan Marko Kujavski","user":"LoanMaikon","type":"user","name":"LoanMaikon"},"summary":"As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot occupancy recognition that requires no labeled samples from the target parking lot. Building upon a self-supervised transfer learning fine-tuning protocol, the proposed training strategy consists of two self-supervised stages: first on unlabeled generic data and then on unlabeled target-specific data, followed by supervised fine-tuning using only generic parking lot labels. We adopt SimCLR with a ResNet-50 encoder and evaluate the method under a leave-one-out cross-environment protocol on three public datasets: PKLot, CNRPark-EXT, and PLds. We also introduce a two-stage deployment strategy in which a Strong General Model is initially deployed, followed by a Specialized Model that incorporates unlabeled images collected during the first N days of deployment in a self-supervised manner. Experimental results show that the Strong General Model alone outperforms supervised and self-supervised baselines, achieving an average accuracy of 97.2%, which further improves to 97.8% with the proposed two-stage strategy. These results demonstrate that self-supervised learning enables a scalable and labelefficient solution for real-world parking occupancy monitoring. Our trained models and source code are publicly available at https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition.","upvotes":0,"discussionId":"6a3a7cebfdcd3514343bb85b","githubRepo":"https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition","githubRepoAddedBy":"user","ai_summary":"A self-supervised transfer learning approach for parking spot occupancy recognition that achieves high accuracy with minimal labeled data through two-stage training and deployment strategies.","ai_keywords":["self-supervised learning","transfer learning","SimCLR","ResNet-50","leave-one-out cross-environment protocol","two-stage deployment strategy","Strong General Model","Specialized Model"],"ai_summary_model":"Qwen/Qwen2.5-Coder-32B-Instruct","githubStars":2},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[],"acceptLanguages":["en"],"markdownContentUrl":"https://huggingface.co/buckets/huggingchat/papers-content/resolve/2606/2606.20886.md","query":{}}">
Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach
Abstract
A self-supervised transfer learning approach for parking spot occupancy recognition that achieves high accuracy with minimal labeled data through two-stage training and deployment strategies.
As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot occupancy recognition that requires no labeled samples from the target parking lot. Building upon a self-supervised transfer learning fine-tuning protocol, the proposed training strategy consists of two self-supervised stages: first on unlabeled generic data and then on unlabeled target-specific data, followed by supervised fine-tuning using only generic parking lot labels. We adopt SimCLR with a ResNet-50 encoder and evaluate the method under a leave-one-out cross-environment protocol on three public datasets: PKLot, CNRPark-EXT, and PLds. We also introduce a two-stage deployment strategy in which a Strong General Model is initially deployed, followed by a Specialized Model that incorporates unlabeled images collected during the first N days of deployment in a self-supervised manner. Experimental results show that the Strong General Model alone outperforms supervised and self-supervised baselines, achieving an average accuracy of 97.2%, which further improves to 97.8% with the proposed two-stage strategy. These results demonstrate that self-supervised learning enables a scalable and labelefficient solution for real-world parking occupancy monitoring. Our trained models and source code are publicly available at https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition.
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Excited to share our paper which introduces self-supervised learning into the field of parking spot occupancy recognition.
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Cite arxiv.org/abs/2606.20886 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2606.20886 in a Space README.md to link it from this page.
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