Datasets:
image
imagewidth (px) 512
512
| file_name
stringclasses 16
values | path_index
int64 10k
108k
| node_index
int64 0
15
| split
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float64 40.7
40.8
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float64 -73.89
-73.79
| month
int64 1
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| year
int64 2.01k
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| move
float64 0
48.6
| heading
float64 0
360
| month_action
int64 0
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| year_action
int64 -12
12
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node_0.jpg
| 10,000 | 0 |
train
| 40.73795 | -73.79939 | 7 | 2,019 | 0.2 | 93.3 | 1 | 3 |
|
node_1.jpg
| 10,000 | 1 |
train
| 40.73795 | -73.79939 | 8 | 2,022 | 0.5 | 92.7 | 1 | -9 |
|
node_2.jpg
| 10,000 | 2 |
train
| 40.73795 | -73.79938 | 9 | 2,013 | 0.2 | 92.2 | 0 | -2 |
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node_3.jpg
| 10,000 | 3 |
train
| 40.73795 | -73.79938 | 9 | 2,011 | 0.2 | 94 | 11 | 7 |
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node_4.jpg
| 10,000 | 4 |
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| 40.73795 | -73.79938 | 8 | 2,018 | 9 | 92.7 | 0 | 4 |
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node_5.jpg
| 10,000 | 5 |
train
| 40.73795 | -73.79927 | 8 | 2,022 | 9.8 | 92.7 | 0 | 0 |
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node_6.jpg
| 10,000 | 6 |
train
| 40.73794 | -73.79915 | 8 | 2,022 | 10.1 | 93.2 | 2 | 0 |
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node_7.jpg
| 10,000 | 7 |
train
| 40.73794 | -73.79903 | 10 | 2,022 | 11.3 | 233 | 0 | 0 |
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node_8.jpg
| 10,000 | 8 |
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| 40.73788 | -73.79914 | 10 | 2,022 | 7.4 | 351.8 | 10 | 0 |
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node_9.jpg
| 10,000 | 9 |
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node_10.jpg
| 10,000 | 10 |
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node_11.jpg
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node_12.jpg
| 10,000 | 12 |
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| 40.73794 | -73.79915 | 8 | 2,022 | 9.8 | 272.6 | 0 | 0 |
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node_13.jpg
| 10,000 | 13 |
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| 40.73795 | -73.79927 | 8 | 2,022 | 9 | 272.8 | 0 | -4 |
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node_14.jpg
| 10,000 | 14 |
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| 40.73795 | -73.79938 | 8 | 2,018 | 0.2 | 274.1 | 1 | -7 |
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node_15.jpg
| 10,000 | 15 |
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node_0.jpg
| 100,000 | 0 |
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node_1.jpg
| 100,000 | 1 |
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node_2.jpg
| 100,000 | 2 |
train
| 40.74616 | -73.87739 | 4 | 2,012 | 0.2 | 299.6 | 6 | 9 |
|
node_3.jpg
| 100,000 | 3 |
train
| 40.74617 | -73.87739 | 10 | 2,021 | 1.1 | 300.5 | 0 | -7 |
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node_4.jpg
| 100,000 | 4 |
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| 40.74617 | -73.87741 | 10 | 2,014 | 0.6 | 300.7 | 1 | 3 |
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node_5.jpg
| 100,000 | 5 |
train
| 40.74617 | -73.87741 | 11 | 2,017 | 0.6 | 300.3 | 9 | -4 |
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node_6.jpg
| 100,000 | 6 |
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| 40.74618 | -73.87742 | 8 | 2,013 | 0.1 | 299.1 | 1 | 9 |
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node_7.jpg
| 100,000 | 7 |
train
| 40.74618 | -73.87742 | 9 | 2,022 | 8.8 | 309.6 | 11 | 2 |
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node_8.jpg
| 100,000 | 8 |
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node_9.jpg
| 100,000 | 9 |
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node_10.jpg
| 100,000 | 10 |
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| 40.74631 | -73.87747 | 8 | 2,024 | 10.6 | 18.9 | 0 | 0 |
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node_11.jpg
| 100,000 | 11 |
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| 40.7464 | -73.87743 | 8 | 2,024 | 13.5 | 23.1 | 0 | 0 |
|
node_12.jpg
| 100,000 | 12 |
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| 40.74652 | -73.87737 | 8 | 2,024 | 10.1 | 24.1 | 0 | 0 |
|
node_13.jpg
| 100,000 | 13 |
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| 40.7466 | -73.87732 | 8 | 2,024 | 12.9 | 24.1 | 0 | 0 |
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node_14.jpg
| 100,000 | 14 |
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| 40.74671 | -73.87726 | 8 | 2,024 | 10.6 | 24.1 | 0 | 0 |
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node_15.jpg
| 100,000 | 15 |
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| 40.74679 | -73.8772 | 8 | 2,024 | 0 | 0 | 0 | 0 |
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node_0.jpg
| 100,001 | 0 |
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| 40.74612 | -73.87728 | 8 | 2,024 | 10.4 | 302.3 | 0 | 0 |
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node_1.jpg
| 100,001 | 1 |
train
| 40.74617 | -73.87738 | 8 | 2,024 | 1.1 | 225.7 | 8 | -12 |
|
node_2.jpg
| 100,001 | 2 |
train
| 40.74616 | -73.87739 | 4 | 2,012 | 0.2 | 299.6 | 6 | 9 |
|
node_3.jpg
| 100,001 | 3 |
train
| 40.74617 | -73.87739 | 10 | 2,021 | 1.1 | 300.5 | 0 | -7 |
|
node_4.jpg
| 100,001 | 4 |
train
| 40.74617 | -73.87741 | 10 | 2,014 | 0.6 | 300.7 | 1 | 3 |
|
node_5.jpg
| 100,001 | 5 |
train
| 40.74617 | -73.87741 | 11 | 2,017 | 0.6 | 300.3 | 9 | -4 |
|
node_6.jpg
| 100,001 | 6 |
train
| 40.74618 | -73.87742 | 8 | 2,013 | 0.1 | 299.1 | 1 | 9 |
|
node_7.jpg
| 100,001 | 7 |
train
| 40.74618 | -73.87742 | 9 | 2,022 | 8.8 | 309.6 | 11 | 2 |
|
node_8.jpg
| 100,001 | 8 |
train
| 40.74623 | -73.8775 | 8 | 2,024 | 6 | 299.8 | 0 | 0 |
|
node_9.jpg
| 100,001 | 9 |
train
| 40.74626 | -73.87756 | 8 | 2,024 | 11.3 | 299.3 | 0 | 0 |
|
node_10.jpg
| 100,001 | 10 |
train
| 40.7463 | -73.87768 | 8 | 2,024 | 10.5 | 299.3 | 0 | 0 |
|
node_11.jpg
| 100,001 | 11 |
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| 40.74635 | -73.87779 | 8 | 2,024 | 9.9 | 299.3 | 0 | 0 |
|
node_12.jpg
| 100,001 | 12 |
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| 40.74639 | -73.87789 | 8 | 2,024 | 9.8 | 299.3 | 0 | 0 |
|
node_13.jpg
| 100,001 | 13 |
train
| 40.74644 | -73.87799 | 8 | 2,024 | 9.8 | 299.3 | 0 | 0 |
|
node_14.jpg
| 100,001 | 14 |
train
| 40.74648 | -73.87809 | 8 | 2,024 | 10 | 299.3 | 0 | 0 |
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node_15.jpg
| 100,001 | 15 |
train
| 40.74653 | -73.8782 | 8 | 2,024 | 0 | 0 | 0 | 0 |
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node_0.jpg
| 100,002 | 0 |
train
| 40.74612 | -73.87728 | 8 | 2,024 | 9 | 122.3 | 0 | 0 |
|
node_1.jpg
| 100,002 | 1 |
train
| 40.74608 | -73.87719 | 8 | 2,024 | 9.5 | 122.3 | 0 | 0 |
|
node_2.jpg
| 100,002 | 2 |
train
| 40.74603 | -73.87709 | 8 | 2,024 | 9.5 | 120.9 | 0 | 0 |
|
node_3.jpg
| 100,002 | 3 |
train
| 40.74599 | -73.87699 | 8 | 2,024 | 9.5 | 120.5 | 0 | 0 |
|
node_4.jpg
| 100,002 | 4 |
train
| 40.74594 | -73.8769 | 8 | 2,024 | 9.6 | 120.4 | 0 | 0 |
|
node_5.jpg
| 100,002 | 5 |
train
| 40.7459 | -73.8768 | 8 | 2,024 | 6.7 | 120.4 | 2 | -10 |
|
node_6.jpg
| 100,002 | 6 |
train
| 40.74587 | -73.87673 | 10 | 2,014 | 1.3 | 120.4 | 6 | -2 |
|
node_7.jpg
| 100,002 | 7 |
train
| 40.74586 | -73.87671 | 4 | 2,012 | 0.1 | 120.3 | 6 | 10 |
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node_8.jpg
| 100,002 | 8 |
train
| 40.74586 | -73.87671 | 10 | 2,022 | 0 | 100.6 | 1 | -5 |
|
node_9.jpg
| 100,002 | 9 |
train
| 40.74586 | -73.87671 | 11 | 2,017 | 0.4 | 120.7 | 10 | -4 |
|
node_10.jpg
| 100,002 | 10 |
train
| 40.74586 | -73.87671 | 9 | 2,013 | 1 | 120.5 | 1 | 8 |
|
node_11.jpg
| 100,002 | 11 |
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| 40.74586 | -73.8767 | 10 | 2,021 | 0.5 | 122.2 | 10 | 3 |
|
node_12.jpg
| 100,002 | 12 |
train
| 40.74585 | -73.87669 | 8 | 2,024 | 9.9 | 120.4 | 0 | 0 |
|
node_13.jpg
| 100,002 | 13 |
train
| 40.74581 | -73.87659 | 8 | 2,024 | 10.1 | 120.4 | 0 | 0 |
|
node_14.jpg
| 100,002 | 14 |
train
| 40.74576 | -73.87649 | 8 | 2,024 | 6.7 | 120 | 2 | -10 |
|
node_15.jpg
| 100,002 | 15 |
train
| 40.74573 | -73.87642 | 10 | 2,014 | 0 | 0 | 0 | 0 |
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node_0.jpg
| 100,003 | 0 |
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| 40.74617 | -73.87738 | 8 | 2,024 | 1.1 | 225.7 | 8 | -12 |
|
node_1.jpg
| 100,003 | 1 |
train
| 40.74616 | -73.87739 | 4 | 2,012 | 0.2 | 299.6 | 6 | 9 |
|
node_2.jpg
| 100,003 | 2 |
train
| 40.74617 | -73.87739 | 10 | 2,021 | 1.1 | 300.5 | 0 | -7 |
|
node_3.jpg
| 100,003 | 3 |
train
| 40.74617 | -73.87741 | 10 | 2,014 | 0.6 | 300.7 | 1 | 3 |
|
node_4.jpg
| 100,003 | 4 |
train
| 40.74617 | -73.87741 | 11 | 2,017 | 0.6 | 300.3 | 9 | -4 |
|
node_5.jpg
| 100,003 | 5 |
train
| 40.74618 | -73.87742 | 8 | 2,013 | 0.1 | 299.1 | 1 | 9 |
|
node_6.jpg
| 100,003 | 6 |
train
| 40.74618 | -73.87742 | 9 | 2,022 | 8.8 | 309.6 | 11 | 2 |
|
node_7.jpg
| 100,003 | 7 |
train
| 40.74623 | -73.8775 | 8 | 2,024 | 15.4 | 207.5 | 0 | 0 |
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node_8.jpg
| 100,003 | 8 |
train
| 40.7461 | -73.87758 | 8 | 2,024 | 10.3 | 203.8 | 0 | 0 |
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node_9.jpg
| 100,003 | 9 |
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| 40.74602 | -73.87763 | 8 | 2,024 | 9.4 | 203.7 | 0 | 0 |
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node_10.jpg
| 100,003 | 10 |
train
| 40.74594 | -73.87768 | 8 | 2,024 | 9.5 | 203.8 | 0 | 0 |
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node_11.jpg
| 100,003 | 11 |
train
| 40.74586 | -73.87773 | 8 | 2,024 | 9.9 | 203.8 | 0 | 0 |
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node_12.jpg
| 100,003 | 12 |
train
| 40.74578 | -73.87777 | 8 | 2,024 | 10.5 | 203.7 | 0 | 0 |
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node_13.jpg
| 100,003 | 13 |
train
| 40.7457 | -73.87782 | 8 | 2,024 | 8.9 | 203.8 | 0 | 0 |
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node_14.jpg
| 100,003 | 14 |
train
| 40.74562 | -73.87787 | 8 | 2,024 | 13.6 | 283.8 | 0 | 0 |
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node_15.jpg
| 100,003 | 15 |
train
| 40.74565 | -73.87802 | 8 | 2,024 | 0 | 0 | 0 | 0 |
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node_0.jpg
| 100,004 | 0 |
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| 40.74617 | -73.87738 | 8 | 2,024 | 1.1 | 225.7 | 8 | -12 |
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node_1.jpg
| 100,004 | 1 |
train
| 40.74616 | -73.87739 | 4 | 2,012 | 0.2 | 299.6 | 6 | 9 |
|
node_2.jpg
| 100,004 | 2 |
train
| 40.74617 | -73.87739 | 10 | 2,021 | 1.1 | 300.5 | 0 | -7 |
|
node_3.jpg
| 100,004 | 3 |
train
| 40.74617 | -73.87741 | 10 | 2,014 | 0.6 | 300.7 | 1 | 3 |
|
node_4.jpg
| 100,004 | 4 |
train
| 40.74617 | -73.87741 | 11 | 2,017 | 0.6 | 300.3 | 9 | -4 |
|
node_5.jpg
| 100,004 | 5 |
train
| 40.74618 | -73.87742 | 8 | 2,013 | 0.1 | 299.1 | 1 | 9 |
|
node_6.jpg
| 100,004 | 6 |
train
| 40.74618 | -73.87742 | 9 | 2,022 | 8.8 | 309.6 | 11 | 2 |
|
node_7.jpg
| 100,004 | 7 |
train
| 40.74623 | -73.8775 | 8 | 2,024 | 15.4 | 207.5 | 0 | 0 |
|
node_8.jpg
| 100,004 | 8 |
train
| 40.7461 | -73.87758 | 8 | 2,024 | 10.3 | 203.8 | 0 | 0 |
|
node_9.jpg
| 100,004 | 9 |
train
| 40.74602 | -73.87763 | 8 | 2,024 | 9.4 | 203.7 | 0 | 0 |
|
node_10.jpg
| 100,004 | 10 |
train
| 40.74594 | -73.87768 | 8 | 2,024 | 9.5 | 203.8 | 0 | 0 |
|
node_11.jpg
| 100,004 | 11 |
train
| 40.74586 | -73.87773 | 8 | 2,024 | 9.9 | 203.8 | 0 | 0 |
|
node_12.jpg
| 100,004 | 12 |
train
| 40.74578 | -73.87777 | 8 | 2,024 | 10.5 | 203.7 | 0 | 0 |
|
node_13.jpg
| 100,004 | 13 |
train
| 40.7457 | -73.87782 | 8 | 2,024 | 8.9 | 203.8 | 0 | 0 |
|
node_14.jpg
| 100,004 | 14 |
train
| 40.74562 | -73.87787 | 8 | 2,024 | 8.5 | 203.6 | 2 | -2 |
|
node_15.jpg
| 100,004 | 15 |
train
| 40.74555 | -73.87791 | 10 | 2,022 | 0 | 0 | 0 | 0 |
|
node_0.jpg
| 100,005 | 0 |
train
| 40.74617 | -73.87738 | 8 | 2,024 | 1.1 | 225.7 | 8 | -12 |
|
node_1.jpg
| 100,005 | 1 |
train
| 40.74616 | -73.87739 | 4 | 2,012 | 0.2 | 299.6 | 6 | 9 |
|
node_2.jpg
| 100,005 | 2 |
train
| 40.74617 | -73.87739 | 10 | 2,021 | 1.1 | 300.5 | 0 | -7 |
|
node_3.jpg
| 100,005 | 3 |
train
| 40.74617 | -73.87741 | 10 | 2,014 | 0.6 | 300.7 | 1 | 3 |
STRIDE
We develop STRIDE (Spatio-Temporal Road Image Dataset for Exploration), that consists of approximately 82B tokens which were arranged into a total of 6M visual "sentences" or token sequences. The sequences are generated from a relatively small set of 131k panoramic images, along with their metadata and openly available highway system data. Our approach enables a 27x increase in information, allowing for generative world model training. In essence, STRIDE turns real street-view panoramic observations into a navigable, interactive environment suitable for free-play across space and time.
For ease of use and data exploration, we prepared this sample of 10k detokenized paths, which amounts to about 100k projected panoramic images along with its corresponding metadata.
Breakdown
Dataset (STRIDE)
The full tokenized dataset is made available through two downloadable files in a public GCS bucket:
gsutil -m cp gs://tera-tardis/STRIDE-1/training.jsonl . # ~327GB
gsutil -m cp gs://tera-tardis/STRIDE-1/testing.jsonl . # ~9GB
In the future, the fully detokenized dataset will be made available. Should you need it, feel free to contact the authors.
San Mateo Coverage Map
Above is the 70km^2 area we selected for putting together the Google StreetView data using openly available road data. Each directly connected component of the graph is represented with a distinct color, for ease of visualization.
Queens Coverage Map
Above is an additional Queens, NY 27km^2 area. Each directly connected component of the graph is represented with a distinct color, for ease of visualization.
Code (TARDIS)
The code used for training of the model is available on GitHub.
Checkpoints (TARDIS)
The checkpoint/state used for evaluation of the model was saved in MessagePack format and is made available through this downloadable file:
gsutil -m cp gs://tera-tardis/STRIDE-1/checkpoint.msgpack . # ~10GB
Should you need other checkpoints, feel free to contact the authors.
Project Website
The project website is available here.
Contacts
- Héctor Carrión
- Yutong Bai
- Víctor A. Hernández Castro
- Kishan Panaganti
- Ayush Zenith
- Matthew Trang
- Tony Zhang
Paper
TARDIS STRIDE: A Spatio-Temporal Road Image Dataset for Exploration and Autonomy
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