<table><caption>TABLE A8: Results for FiveBillionPixels dataset, best results in **bold**, second best **underlined**.</caption><thead><tr><th>Model</th><th>mIoU ↑</th><th>m-F1 ↑</th><th>m-prec ↑</th><th>m-rec ↑</th><th>m-acc ↑</th></tr></thead><tbody><tr><td>CROMA</td><td>51.85</td><td>63.50</td><td>59.96</td><td>77.34</td><td>90.14</td></tr><tr><td>DOFA</td><td>43.80</td><td>54.14</td><td>51.22</td><td>66.71</td><td>89.00</td></tr><tr><td>GFM-Swin</td><td>67.19</td><td>77.67</td><td><u>76.28</u></td><td>80.30</td><td><u>98.12</u></td></tr><tr><td>Prithvi</td><td>46.82</td><td>58.15</td><td>55.39</td><td>70.83</td><td>91.20</td></tr><tr><td>RemoteCLIP</td><td><b>69.19</b></td><td><b>79.18</b></td><td><b>77.64</b></td><td><u>82.57</u></td><td><b>98.31</b></td></tr><tr><td>SatlasNet</td><td>50.97</td><td>62.89</td><td>59.94</td><td>70.63</td><td>93.18</td></tr><tr><td>Scale-MAE</td><td><u>67.19</u></td><td><u>78.12</u></td><td>75.10</td><td><b>82.28</b></td><td>98.05</td></tr><tr><td>SpectralGPT</td><td>33.42</td><td>42.15</td><td>41.76</td><td>48.11</td><td>85.24</td></tr><tr><td>S12-Data2Vec</td><td>48.82</td><td>59.53</td><td>56.85</td><td>69.26</td><td>92.73</td></tr><tr><td>S12-DINO</td><td>51.15</td><td>62.26</td><td>59.51</td><td>71.20</td><td>93.53</td></tr><tr><td>S12-MAE</td><td>51.50</td><td>62.69</td><td>59.79</td><td>69.98</td><td>93.43</td></tr><tr><td>S12-MoCo</td><td>53.42</td><td>64.40</td><td>61.80</td><td>69.97</td><td>94.32</td></tr><tr><td>UNet Baseline</td><td>60.68</td><td>70.92</td><td>70.14</td><td>72.80</td><td>97.27</td></tr><tr><td>ViT Baseline</td><td>59.32</td><td>69.54</td><td>68.95</td><td>71.42</td><td>93.28</td></tr></tbody></table>

<figure><img src="image_16.png" /><figcaption>Fig. A7: Example time series data of **DynamicEarthNet**. (Location: Global. Domain: Urban Land Cover.) The images sequences are visualized with B4, B3, B2 bands and sampled from the first 6 dates of every month to reduce computation requirement. This dataset challenges the models by extremely subtle daily changes.</figcaption></figure>

<table><caption>TABLE A9: DynamicEarthNet results using 100% of the dataset, comparing linear and L-TAE temporal mapping strategies. Best results are in **bold**, second best are **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">mIoU ↑</th><th colspan="2">m-F1 ↑</th><th colspan="2">m-prec ↑</th><th colspan="2">m-rec ↑</th><th colspan="2">m-acc ↑</th></tr><tr><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th></tr></thead><tbody><tr><td>CROMA</td><td>38.25</td><td>38.29</td><td>47.14</td><td>47.31</td><td>46.70</td><td>47.08</td><td>49.84</td><td>51.42</td><td>69.54</td><td>69.72</td></tr><tr><td>DOFA</td><td><b>40.84</b></td><td>39.29</td><td><b>51.21</b></td><td>49.52</td><td><b>50.38</b></td><td>48.06</td><td>53.49</td><td><u>56.75</u></td><td><u>70.03</u></td><td>69.82</td></tr><tr><td>GFM-Swin</td><td>35.78</td><td>34.09</td><td>46.59</td><td>46.41</td><td>45.33</td><td>43.44</td><td>49.77</td><td>54.88</td><td>66.51</td><td>66.19</td></tr><tr><td>Prithvi</td><td>28.42</td><td>27.86</td><td>39.33</td><td>38.74</td><td>37.62</td><td>37.95</td><td>47.36</td><td>42.81</td><td>63.18</td><td>61.49</td></tr><tr><td>RemoteCLIP</td><td>34.07</td><td>31.78</td><td>45.70</td><td>43.79</td><td>43.71</td><td>42.24</td><td>50.88</td><td>52.18</td><td>66.27</td><td>64.27</td></tr><tr><td>SatlasNet</td><td></td><td>36.31</td><td></td><td>45.45</td><td></td><td>44.69</td><td></td><td>47.39</td><td></td><td>67.22</td></tr><tr><td>Scale-MAE</td><td>35.47</td><td>35.11</td><td>48.86</td><td>48.47</td><td>46.59</td><td>46.21</td><td>53.65</td><td><b>60.62</b></td><td>66.44</td><td>66.30</td></tr><tr><td>SpectralGPT</td><td>39.27</td><td>37.85</td><td>49.42</td><td>47.76</td><td><u>49.71</u></td><td>47.38</td><td>50.31</td><td>51.03</td><td>69.24</td><td>69.39</td></tr><tr><td>S12-MoCo</td><td>36.53</td><td>35.44</td><td>44.94</td><td>44.02</td><td>44.63</td><td>44.60</td><td>48.12</td><td>45.88</td><td>68.43</td><td>67.84</td></tr><tr><td>S12-DINO</td><td>33.80</td><td>34.81</td><td>40.69</td><td>43.89</td><td>42.79</td><td>44.15</td><td>43.53</td><td>47.19</td><td>68.90</td><td>66.00</td></tr><tr><td>S12-MAE</td><td>35.23</td><td>34.08</td><td>43.18</td><td>41.06</td><td>43.32</td><td>42.32</td><td>45.89</td><td>46.90</td><td>67.98</td><td>67.42</td></tr><tr><td>S12-Data2Vec</td><td>36.41</td><td>35.90</td><td>44.32</td><td>44.79</td><td>44.54</td><td>45.61</td><td>48.51</td><td>45.21</td><td>69.28</td><td>66.85</td></tr><tr><td>UNet Baseline</td><td></td><td><u>39.46</u></td><td></td><td>48.81</td><td></td><td>47.94</td><td></td><td>52.58</td><td></td><td><b>70.47</b></td></tr><tr><td>ViT Baseline</td><td>36.83</td><td>36.38</td><td><u>49.84</u></td><td>49.33</td><td>48.07</td><td>47.35</td><td>53.68</td><td>54.25</td><td>66.75</td><td>67.02</td></tr></tbody></table>
<table><caption>TABLE A10: Results for DynamicEarthNet dataset with linear time strategy, best results in **bold**, second best **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">mIoU ↑</th><th colspan="3">m-F1 ↑</th><th colspan="3">m-prec ↑</th><th colspan="3">m-rec ↑</th><th colspan="3">m-acc ↑</th></tr><tr><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th></tr></thead><tbody><tr><td>CROMA</td><td>36.72</td><td>38.94</td><td>38.25</td><td>45.54</td><td>48.57</td><td>47.14</td><td>45.20</td><td>47.93</td><td>46.70</td><td>49.89</td><td>51.29</td><td>49.84</td><td>68.89</td><td><u>69.42</u></td><td>69.54</td></tr><tr><td>DOFA</td><td><u>39.81</u></td><td><b>40.16</b></td><td><b>40.84</b></td><td><u>50.82</u></td><td><u>50.33</u></td><td><b>51.21</b></td><td><u>49.63</u></td><td><u>49.24</u></td><td><b>50.38</b></td><td><u>53.20</u></td><td><u>55.92</u></td><td>53.49</td><td><u>69.15</u></td><td><b>70.38</b></td><td><u>70.03</u></td></tr><tr><td>GFM-Swin</td><td>33.73</td><td>33.05</td><td>35.78</td><td>44.25</td><td>44.28</td><td>46.59</td><td>43.72</td><td>43.05</td><td>45.33</td><td>46.80</td><td>47.09</td><td>49.77</td><td>64.96</td><td>64.55</td><td>66.51</td></tr><tr><td>Prithvi</td><td>29.63</td><td>29.88</td><td>28.42</td><td>39.06</td><td>39.81</td><td>39.33</td><td>38.18</td><td>38.43</td><td>37.62</td><td>44.73</td><td>46.60</td><td>47.36</td><td>63.78</td><td>64.45</td><td>63.18</td></tr><tr><td>RemoteCLIP</td><td>35.62</td><td>37.17</td><td>34.07</td><td>46.86</td><td>48.92</td><td>45.70</td><td>45.44</td><td>48.85</td><td>43.71</td><td>52.24</td><td>53.17</td><td>50.88</td><td>66.64</td><td>67.05</td><td>66.27</td></tr><tr><td>SatlasNet</td><td>34.64</td><td>36.34</td><td>36.31</td><td>44.20</td><td>45.11</td><td>45.45</td><td>44.29</td><td>44.68</td><td>44.69</td><td>46.29</td><td>47.30</td><td>47.39</td><td>67.22</td><td>67.65</td><td>67.22</td></tr><tr><td>Scale-MAE</td><td>37.11</td><td>35.46</td><td>35.47</td><td>48.88</td><td>48.21</td><td>48.86</td><td>47.06</td><td>46.93</td><td>46.59</td><td>52.22</td><td>53.61</td><td><u>53.65</u></td><td>67.27</td><td>66.23</td><td>66.44</td></tr><tr><td>SpectralGPT</td><td>35.91</td><td>36.73</td><td>38.28</td><td>44.43</td><td>46.22</td><td>48.14</td><td>45.41</td><td>46.44</td><td>47.07</td><td>49.86</td><td>47.37</td><td>51.01</td><td><b>69.75</b></td><td>68.14</td><td>68.65</td></tr><tr><td>S12-MoCo</td><td>32.38</td><td>35.59</td><td>36.53</td><td>40.47</td><td>43.57</td><td>44.94</td><td>41.15</td><td>43.79</td><td>44.63</td><td>42.98</td><td>48.47</td><td>48.12</td><td>66.07</td><td>68.43</td><td>68.43</td></tr><tr><td>S12-DINO</td><td>34.03</td><td>34.03</td><td>33.80</td><td>41.64</td><td>41.40</td><td>40.69</td><td>43.00</td><td>42.26</td><td>42.79</td><td>41.50</td><td>48.66</td><td>43.53</td><td>67.53</td><td>67.35</td><td>68.90</td></tr><tr><td>S12-MAE</td><td>33.62</td><td>35.40</td><td>35.23</td><td>42.15</td><td>43.23</td><td>41.06</td><td>42.92</td><td>43.57</td><td>43.32</td><td>43.26</td><td>48.17</td><td>45.89</td><td>66.31</td><td>68.27</td><td>67.98</td></tr><tr><td>S12-Data2Vec</td><td>34.71</td><td>35.36</td><td>36.41</td><td>41.77</td><td>42.85</td><td>44.79</td><td>42.89</td><td>43.64</td><td>44.54</td><td>47.52</td><td>49.81</td><td>48.51</td><td>68.35</td><td>68.95</td><td>69.28</td></tr><tr><td>UNet Baseline</td><td>35.59</td><td>35.14</td><td>39.46</td><td>44.45</td><td>44.87</td><td>48.81</td><td>45.69</td><td>44.59</td><td><u>47.94</u></td><td>45.47</td><td>48.09</td><td>52.58</td><td><u>68.38</u></td><td>67.82</td><td><b>70.47</b></td></tr><tr><td>ViT Baseline</td><td><b>39.89</b></td><td><u>39.26</u></td><td>36.83</td><td><b>52.77</b></td><td><b>51.79</b></td><td><u>49.84</u></td><td><b>50.40</b></td><td><b>49.81</b></td><td>48.07</td><td><b>56.85</b></td><td><b>56.67</b></td><td>53.68</td><td>68.55</td><td>69.19</td><td>66.75</td></tr></tbody></table>

<table><caption>TABLE A11: Results for DynamicEarthNet dataset with L-TAE time strategy, best results in **bold**, second best **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">mIoU ↑</th><th colspan="3">m-F1 ↑</th><th colspan="3">m-prec ↑</th><th colspan="3">m-rec ↑</th><th colspan="3">m-acc ↑</th></tr><tr><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th></tr></thead><tbody><tr><td>CROMA</td><td>36.08</td><td>38.30</td><td>38.29</td><td>45.46</td><td>47.47</td><td>47.31</td><td>48.04</td><td>46.69</td><td>47.07</td><td>51.17</td><td>52.97</td><td>51.42</td><td>66.22</td><td><u>68.89</u></td><td>69.72</td></tr><tr><td>DOFA</td><td><b>39.15</b></td><td><u>39.20</u></td><td><u>39.29</u></td><td><b>48.75</b></td><td><b>50.53</b></td><td><b>49.52</b></td><td><b>50.21</b></td><td><b>49.32</b></td><td><b>48.06</b></td><td>53.52</td><td>55.11</td><td><u>56.75</u></td><td><b>69.64</b></td><td><b>70.10</b></td><td><u>69.82</u></td></tr><tr><td>GFM-Swin</td><td>28.16</td><td>31.25</td><td>34.09</td><td>39.72</td><td>43.18</td><td>46.41</td><td>38.55</td><td>41.57</td><td>43.44</td><td>51.04</td><td>62.93</td><td>54.88</td><td>61.85</td><td>64.95</td><td>66.19</td></tr><tr><td>Prithvi</td><td>32.28</td><td>33.42</td><td>27.86</td><td>42.80</td><td>43.35</td><td>38.74</td><td>41.19</td><td>43.35</td><td>37.95</td><td>46.95</td><td>45.22</td><td>42.81</td><td>64.15</td><td>66.04</td><td>61.49</td></tr><tr><td>RemoteCLIP</td><td>34.43</td><td>30.91</td><td>31.78</td><td>46.01</td><td>42.06</td><td>43.79</td><td>43.29</td><td>39.90</td><td>42.24</td><td><b>57.38</b></td><td>50.05</td><td>52.18</td><td>66.72</td><td>64.30</td><td>64.27</td></tr><tr><td>SatlasNet</td><td>34.64</td><td>36.34</td><td>36.31</td><td>44.20</td><td>45.11</td><td>45.45</td><td>44.29</td><td>44.68</td><td>44.69</td><td>46.29</td><td>47.30</td><td>47.39</td><td>67.22</td><td>67.65</td><td>67.22</td></tr><tr><td>Scale-MAE</td><td>35.27</td><td>35.57</td><td>35.11</td><td>45.95</td><td>48.33</td><td>48.47</td><td>44.71</td><td>48.41</td><td>46.21</td><td>50.35</td><td><b>61.09</b></td><td><b>60.62</b></td><td>67.02</td><td>65.12</td><td>66.30</td></tr><tr><td>SpectralGPT</td><td>35.33</td><td>36.52</td><td>37.85</td><td>45.17</td><td>45.51</td><td>47.76</td><td>46.06</td><td>46.12</td><td>47.38</td><td>45.07</td><td>46.07</td><td>51.03</td><td>65.41</td><td>66.84</td><td>69.39</td></tr><tr><td>S12-MoCo</td><td>32.24</td><td>34.45</td><td>35.44</td><td>40.03</td><td>42.62</td><td>44.02</td><td>41.40</td><td>43.35</td><td>44.60</td><td>45.94</td><td>46.75</td><td>45.88</td><td>64.31</td><td>66.47</td><td>67.84</td></tr><tr><td>S12-DINO</td><td>32.78</td><td>32.76</td><td>34.81</td><td>41.99</td><td>40.99</td><td>43.89</td><td>44.04</td><td>42.62</td><td>44.15</td><td>40.67</td><td>45.52</td><td>47.19</td><td>65.46</td><td>65.82</td><td>66.00</td></tr><tr><td>S12-MAE</td><td>30.59</td><td>33.29</td><td>34.08</td><td>38.76</td><td>40.37</td><td>41.06</td><td>40.86</td><td>41.98</td><td>42.32</td><td>40.74</td><td>49.48</td><td>46.90</td><td>67.42</td><td>67.15</td><td>67.42</td></tr><tr><td>S12-Data2Vec</td><td>33.63</td><td>32.61</td><td>35.90</td><td>41.73</td><td>40.05</td><td>44.79</td><td>43.56</td><td>41.42</td><td>45.61</td><td>42.06</td><td>46.97</td><td>45.21</td><td>67.74</td><td>65.06</td><td>66.85</td></tr><tr><td>UNet Baseline</td><td>35.59</td><td>35.14</td><td><b>39.46</b></td><td>44.45</td><td>44.87</td><td>48.81</td><td>45.69</td><td>44.59</td><td><u>47.94</u></td><td>45.47</td><td>48.09</td><td>52.58</td><td><u>68.38</u></td><td>67.82</td><td><b>70.47</b></td></tr><tr><td>ViT Baseline</td><td>35.39</td><td>37.33</td><td>36.38</td><td>47.87</td><td><u>49.81</u></td><td><u>49.33</u></td><td>45.25</td><td>47.46</td><td>47.35</td><td><u>56.31</u></td><td><u>55.29</u></td><td>54.25</td><td>66.36</td><td>67.58</td><td>67.02</td></tr></tbody></table>

<figure><img src="image_17.png" /><figcaption>Fig. A8: Example time series data of Crop Type Mapping-SS (Location: South Sudan. Domain: Agriculture). The Sentinel-1 images are visualized by taking VH band and the Sentinel-2 images are visualized by taking B4, B3, B2 bands. The last two frames from 2017 are displayed for this example.</figcaption></figure>
<table><caption>TABLE A12: Crop Type Mapping-South Sudan results using 100% of the dataset, comparing linear and L-TAE temporal mapping strategies. Best results are in **bold**, second best are **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Modality</th><th colspan="2">mIoU ↑</th><th colspan="2">m-F1 ↑</th><th colspan="2">m-prec ↑</th><th colspan="2">m-rec ↑</th><th colspan="2">m-acc ↑</th></tr><tr><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th></tr></thead><tbody><tr><td>CROMA</td><td>joint</td><td>38.91</td><td>43.86</td><td>48.88</td><td>55.90</td><td>51.85</td><td>58.03</td><td>46.93</td><td>58.54</td><td>73.66</td><td>80.31</td></tr><tr><td>CROMA</td><td>optical</td><td>47.02</td><td>49.38</td><td>57.34</td><td>61.96</td><td>56.65</td><td>70.43</td><td>50.58</td><td>62.28</td><td>72.19</td><td>75.50</td></tr><tr><td>CROMA</td><td>sar</td><td>35.39</td><td>50.44</td><td>44.68</td><td>57.95</td><td>49.90</td><td>59.81</td><td>41.78</td><td>57.93</td><td>68.78</td><td>75.80</td></tr><tr><td>DOFA</td><td>optical</td><td>49.81</td><td>51.33</td><td>57.53</td><td>61.37</td><td>57.24</td><td>60.81</td><td>60.38</td><td>63.82</td><td>74.65</td><td>80.61</td></tr><tr><td>GFM-Swin</td><td>optical</td><td>39.72</td><td>46.98</td><td>48.56</td><td>50.33</td><td>52.10</td><td>51.16</td><td>48.64</td><td>55.60</td><td>75.36</td><td><u>82.50</u></td></tr><tr><td>Prithvi</td><td>optical</td><td>39.92</td><td>43.07</td><td>51.04</td><td>57.92</td><td>56.81</td><td>70.00</td><td>47.31</td><td>54.59</td><td>70.98</td><td>72.48</td></tr><tr><td>RemoteCLIP</td><td>optical</td><td>46.50</td><td>52.05</td><td>54.34</td><td>61.97</td><td>51.86</td><td>63.81</td><td>59.26</td><td>63.13</td><td>79.37</td><td>80.68</td></tr><tr><td>SatlasNet</td><td>optical</td><td>46.97</td><td>46.97</td><td>53.74</td><td>53.74</td><td>53.88</td><td>53.88</td><td>53.62</td><td>53.62</td><td>71.72</td><td>71.72</td></tr><tr><td>Scale-MAE</td><td>optical</td><td>21.39</td><td>25.42</td><td>25.84</td><td>33.92</td><td>26.18</td><td>34.00</td><td>40.79</td><td>36.23</td><td>77.10</td><td>73.43</td></tr><tr><td>SpectralGPT</td><td>optical</td><td>53.50</td><td>46.95</td><td>61.29</td><td>59.11</td><td>60.56</td><td>65.91</td><td>64.84</td><td>63.01</td><td>81.76</td><td>64.69</td></tr><tr><td>S12-Data2Vec</td><td>optical</td><td><u>54.01</u></td><td><u>54.03</u></td><td><u>66.95</u></td><td><u>67.55</u></td><td><u>69.01</u></td><td><u>69.44</u></td><td>66.63</td><td><u>71.02</u></td><td><u>81.92</u></td><td><u>83.57</u></td></tr><tr><td>S12-DINO</td><td>optical</td><td>46.56</td><td>48.66</td><td>59.02</td><td>63.14</td><td>61.48</td><td>72.75</td><td>59.73</td><td>62.97</td><td>79.93</td><td>76.69</td></tr><tr><td>S12-MAE</td><td>optical</td><td>46.28</td><td>45.8</td><td>57.73</td><td>58.38</td><td>60.10</td><td>63.35</td><td>57.72</td><td>58.77</td><td>74.72</td><td>79.03</td></tr><tr><td>S12-MAE</td><td>sar</td><td>28.51</td><td>21.06</td><td>37.68</td><td>30.50</td><td>36.01</td><td>52.05</td><td>40.83</td><td>30.79</td><td>76.87</td><td>54.82</td></tr><tr><td>S12-MoCo</td><td>optical</td><td>44.22</td><td>48.58</td><td>51.38</td><td>58.63</td><td>50.76</td><td>60.47</td><td>52.68</td><td>60.1</td><td>70.5</td><td>74.99</td></tr><tr><td>UNet Baseline</td><td>optical</td><td colspan="2">47.57</td><td colspan="2">54.85</td><td colspan="2">55.23</td><td><u>68.51</u></td><td colspan="2">81.89</td></tr><tr><td>ViT Baseline</td><td>optical</td><td>50.24</td><td>51.78</td><td>57.38</td><td>60.43</td><td>57.47</td><td>61.95</td><td>57.36</td><td>59.52</td><td>74.58</td><td>74.17</td></tr></tbody></table>

<table><caption>TABLE A13: Results for Crop Type Mapping-South Sudan dataset with linear time strategy, best results in **bold**, second best underlined.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Modality</th><th colspan="3">mIoU ↑</th><th colspan="3">m-F1 ↑</th><th colspan="3">m-prec ↑</th><th colspan="3">m-rec ↑</th><th colspan="3">m-acc ↑</th></tr><tr><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th></tr></thead><tbody><tr><td>CROMA</td><td>optical</td><td>27.67</td><td>43.00</td><td>47.02</td><td>39.63</td><td><strong>56.98</strong></td><td>57.34</td><td><strong>57.85</strong></td><td><strong>66.39</strong></td><td>59.98</td><td>37.79</td><td>53.76</td><td>55.18</td><td>58.02</td><td>76.51</td><td>74.71</td></tr><tr><td>DOFA</td><td>optical</td><td>28.03</td><td>39.03</td><td>49.81</td><td>40.16</td><td>51.76</td><td>57.53</td><td>48.69</td><td>60.23</td><td>57.24</td><td>37.79</td><td>47.20</td><td>60.38</td><td>58.20</td><td>71.72</td><td>74.65</td></tr><tr><td>GFM-Swin</td><td>optical</td><td>26.13</td><td>19.73</td><td>42.11</td><td>34.43</td><td>22.05</td><td>45.71</td><td>34.69</td><td>24.45</td><td>49.73</td><td>34.43</td><td>20.08</td><td>42.30</td><td>61.13</td><td><u>78.90</u></td><td><u>82.59</u></td></tr><tr><td>Prithvi</td><td>optical</td><td>24.99</td><td>36.14</td><td>39.92</td><td>35.02</td><td>47.00</td><td>51.04</td><td>45.36</td><td>54.49</td><td>56.81</td><td>32.33</td><td>43.44</td><td>47.31</td><td>58.94</td><td>72.29</td><td>70.98</td></tr><tr><td>RemoteCLIP</td><td>optical</td><td>14.96</td><td>23.53</td><td>46.50</td><td>23.21</td><td>32.36</td><td>54.34</td><td>31.36</td><td>47.13</td><td>51.86</td><td>25.05</td><td>29.99</td><td>59.26</td><td>42.25</td><td>64.73</td><td>79.37</td></tr><tr><td>SatlasNet</td><td>optical</td><td><u>38.80</u></td><td><u>46.37</u></td><td>46.97</td><td>46.60</td><td>51.60</td><td>53.74</td><td>48.54</td><td>51.27</td><td>53.88</td><td><u>45.41</u></td><td><u>52.91</u></td><td>53.62</td><td>65.92</td><td>75.35</td><td>71.72</td></tr><tr><td>Scale-MAE</td><td>optical</td><td>21.89</td><td>21.79</td><td>21.39</td><td>29.83</td><td>27.01</td><td>25.84</td><td>33.71</td><td>27.08</td><td>26.18</td><td>28.02</td><td>28.45</td><td>40.79</td><td><u>66.12</u></td><td>75.14</td><td>77.10</td></tr><tr><td>SpectralGPT</td><td>optical</td><td><strong>45.36</strong></td><td><strong>49.94</strong></td><td><u>53.50</u></td><td><strong>52.62</strong></td><td><u>56.85</u></td><td><u>61.29</u></td><td><u>57.74</u></td><td>55.98</td><td>60.56</td><td><strong>53.19</strong></td><td><strong>58.34</strong></td><td>64.84</td><td>65.32</td><td>77.45</td><td>81.76</td></tr><tr><td>S12-Data2Vec</td><td>optical</td><td>34.51</td><td>35.05</td><td><strong>54.01</strong></td><td>46.66</td><td>48.20</td><td><strong>66.95</strong></td><td>57.41</td><td>57.93</td><td><strong>69.01</strong></td><td>42.98</td><td>45.29</td><td>66.63</td><td>62.37</td><td>66.52</td><td>81.92</td></tr><tr><td>S12-DINO</td><td>optical</td><td>29.30</td><td>34.61</td><td>46.56</td><td>41.53</td><td>46.42</td><td>59.02</td><td>52.03</td><td>57.51</td><td><u>61.48</u></td><td>38.17</td><td>41.66</td><td>59.73</td><td>65.98</td><td>71.11</td><td>79.93</td></tr><tr><td>S12-MAE</td><td>optical</td><td>32.58</td><td>43.21</td><td>46.28</td><td>45.18</td><td>56.17</td><td>57.73</td><td>55.80</td><td><u>60.79</u></td><td>60.10</td><td>41.31</td><td><u>55.55</u></td><td>57.72</td><td>61.20</td><td>78.82</td><td>74.72</td></tr><tr><td>S12-MoCo</td><td>optical</td><td>36.71</td><td>37.41</td><td>44.22</td><td><u>47.02</u></td><td>44.61</td><td>51.38</td><td>53.10</td><td>49.13</td><td>50.76</td><td>43.44</td><td>41.59</td><td>52.68</td><td><strong>69.73</strong></td><td>73.77</td><td>70.50</td></tr><tr><td>UNet Baseline</td><td>optical</td><td>13.88</td><td>36.30</td><td>47.57</td><td>21.10</td><td>43.44</td><td>54.85</td><td>32.42</td><td>47.14</td><td>55.23</td><td>23.06</td><td>42.10</td><td><strong>68.51</strong></td><td>42.51</td><td><strong>79.24</strong></td><td>81.89</td></tr><tr><td>ViT Baseline</td><td>optical</td><td>30.10</td><td>34.41</td><td>50.24</td><td>42.11</td><td>46.39</td><td>57.38</td><td>54.21</td><td>56.45</td><td>57.47</td><td>39.28</td><td>42.61</td><td>57.36</td><td>59.31</td><td>71.98</td><td>74.58</td></tr></tbody></table>

<table><caption>TABLE A14: Results for Crop Type Mapping-South Sudan dataset with L-TAE time strategy, best results in **bold**, second best underlined.</caption><thead><tr><th rowspan="2">Model</th><th rowspan="2">Modality</th><th colspan="3">mIoU ↑</th><th colspan="3">m-F1 ↑</th><th colspan="3">m-prec ↑</th><th colspan="3">m-rec ↑</th><th colspan="3">m-acc ↑</th></tr><tr><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th><th>10%</th><th>50%</th><th>100%</th></tr></thead><tbody><tr><td>CROMA</td><td>optical</td><td>36.77</td><td>42.20</td><td>49.38</td><td><strong>49.76</strong></td><td><strong>56.72</strong></td><td>61.96</td><td><u>60.99</u></td><td><strong>67.83</strong></td><td>71.19</td><td>44.81</td><td><strong>53.13</strong></td><td>59.97</td><td>70.30</td><td>70.07</td><td>68.30</td></tr><tr><td>DOFA</td><td>optical</td><td>29.91</td><td>30.93</td><td>51.33</td><td>43.31</td><td>42.65</td><td>61.37</td><td>59.89</td><td>57.50</td><td>60.81</td><td>39.47</td><td>39.04</td><td><u>63.82</u></td><td>61.53</td><td>69.02</td><td>80.61</td></tr><tr><td>GFM-Swin</td><td>optical</td><td>13.30</td><td>38.58</td><td>46.98</td><td>18.97</td><td>44.74</td><td>50.33</td><td>25.03</td><td>49.63</td><td>51.16</td><td>41.19</td><td>42.98</td><td>55.60</td><td>44.47</td><td><strong>81.12</strong></td><td><strong>82.50</strong></td></tr><tr><td>Prithvi</td><td>optical</td><td>27.71</td><td><u>42.51</u></td><td>43.07</td><td>37.89</td><td>54.18</td><td>57.92</td><td>51.44</td><td><u>60.21</u></td><td>70.00</td><td>38.06</td><td>50.98</td><td>54.59</td><td>70.44</td><td>77.62</td><td>72.48</td></tr><tr><td>RemoteCLIP</td><td>optical</td><td>19.86</td><td>32.09</td><td><u>52.05</u></td><td>30.90</td><td>44.35</td><td>61.97</td><td>43.76</td><td>48.60</td><td>63.81</td><td>32.18</td><td>45.70</td><td>63.13</td><td>46.74</td><td>70.81</td><td>80.68</td></tr><tr><td>SatlasNet</td><td>optical</td><td><strong>38.80</strong></td><td><strong>46.37</strong></td><td>46.97</td><td>46.60</td><td>51.60</td><td>53.74</td><td>48.54</td><td>51.27</td><td>53.88</td><td>45.41</td><td>52.91</td><td>53.62</td><td>65.92</td><td>75.35</td><td>71.72</td></tr><tr><td>Scale-MAE</td><td>optical</td><td>13.44</td><td>20.32</td><td>25.42</td><td>21.91</td><td>27.17</td><td>33.92</td><td>32.64</td><td>25.52</td><td>34.00</td><td>28.74</td><td>30.13</td><td>36.23</td><td>35.30</td><td>63.69</td><td>73.43</td></tr><tr><td>SpectralGPT</td><td>optical</td><td>27.37</td><td>39.92</td><td>46.95</td><td>39.67</td><td>43.98</td><td>59.11</td><td>55.66</td><td>59.21</td><td>65.91</td><td>36.52</td><td><u>52.98</u></td><td>63.01</td><td>61.72</td><td>63.72</td><td>64.69</td></tr><tr><td>S12-Data2Vec</td><td>optical</td><td>34.11</td><td>28.53</td><td><strong>54.03</strong></td><td>45.70</td><td>38.19</td><td><strong>67.55</strong></td><td>58.73</td><td>36.81</td><td>69.44</td><td>44.19</td><td>41.62</td><td>71.02</td><td><strong>75.49</strong></td><td>73.78</td><td><strong>83.57</strong></td></tr><tr><td>S12-DINO</td><td>optical</td><td><u>38.44</u></td><td>31.13</td><td>48.66</td><td>48.74</td><td>42.34</td><td><u>63.14</u></td><td>56.70</td><td>48.11</td><td><strong>72.75</strong></td><td><u>50.19</u></td><td>40.39</td><td>62.97</td><td>72.02</td><td>70.26</td><td>76.69</td></tr><tr><td>S12-MAE</td><td>optical</td><td>35.29</td><td>28.07</td><td>45.80</td><td>48.86</td><td>38.32</td><td>58.38</td><td><strong>62.76</strong></td><td>37.82</td><td>63.35</td><td>48.23</td><td>43.09</td><td>58.77</td><td><u>73.54</u></td><td>71.27</td><td>79.03</td></tr><tr><td>S12-MoCo</td><td>optical</td><td>36.54</td><td>41.32</td><td>48.58</td><td><u>49.33</u></td><td><u>53.95</u></td><td>58.63</td><td>58.60</td><td>56.37</td><td>60.47</td><td><strong>51.51</strong></td><td>53.78</td><td>60.10</td><td>73.16</td><td>78.43</td><td>74.99</td></tr><tr><td>UNet Baseline</td><td>optical</td><td>13.88</td><td>36.30</td><td>47.57</td><td>21.10</td><td>43.44</td><td>54.85</td><td>32.42</td><td>47.14</td><td>55.23</td><td>23.06</td><td>42.10</td><td><strong>68.51</strong></td><td>42.51</td><td><strong>79.24</strong></td><td>81.89</td></tr><tr><td>ViT Baseline</td><td>optical</td><td>17.16</td><td>40.79</td><td>51.78</td><td>23.79</td><td>48.26</td><td>60.43</td><td>25.58</td><td>50.09</td><td>61.95</td><td>26.02</td><td>46.84</td><td>59.52</td><td>55.91</td><td>66.62</td><td>74.17</td></tr></tbody></table>
differences between crop types. Alongside the S12 models, RemoteCLIP and DOFA also achieve strong performance. In general, the L-TAE strategy generally yields superior results than linear temporal mapping, except that SpectralGPT exhibits a significant performance drop with this strategy. Regarding the multi-modality experiments, incorporating SAR data does not improve performance and even leads to a decrease compared to using optical data alone. In addition, Scale-MAE, pre-trained on high-resolution imagery, shows the lowest performance on this dataset.

Tables A13 and A14 showcase the data scarcity scenarios for linear and L-TAE strategies, respectively, utilizing only optical information. In these cases, the performance of the fully supervised baseline drops considerably compared to foundation models. Specifically, CROMA, SatlasNet, and the S12 models significantly outperform the baseline, demonstrating their robustness in limited data settings. This highlights the advantage of foundation models in leveraging learned representations to achieve strong performance even with reduced training data. For the linear mapping strategy, SpectralGPT consistently performs well across different label scarcity settings, further emphasizing the importance of spectral information for accurate crop type mapping.

## I. SpaceNet 7

The SpaceNet 7 dataset features satellite image time series acquired by the PlanetScope constellation between 2017 and 2020 for 80 sites spread across the globe [176]. Each time series consists of 18 to 26 monthly image mosaics with a size of 1024 × 1024 pixels and a spatial resolution of approximately 4 m. In addition, the SpaceNet 7 dataset provides manually annotated building footprints for the 60 training sites and corresponding masks denoting areas that are obscured by clouds. Although the task of the original SpaceNet 7 challenge was to track these building footprints (i.e. vector data), the SpaceNet 7 dataset is commonly used to evaluate urban change detection methods. A data sample is shown in Fig. A9.

The labeled SpaceNet 7 sites (n = 60) were first assigned to the training (n = 30), validation (n = 15), or test (n = 15) set, following the split in [177]. Then, sites were tiled into crops of size 256 × 256 pixels. During training, samples for a crop are generated by randomly selecting two timestamps from the image time series with a minimal temporal gap of five timestamps. The rasterized building labels (see [177]) for these timestamps were used to compute the change label. For model evaluation, the first and the last cloud-free images of a time series were selected.

1) *Extensive Results:* Table A15 lists extensive change detection results on the SpaceNet 7 dataset. We compare two strategies, namely concatenation and differencing, to detect changes from the bi-temporal feature pairs extracted by the Siamese encoder (i.e. shared encoders). More specifically, the former strategy uses the concatenated feature pairs as input to the decoder, while the latter strategy uses the subtraction of the feature pairs. Scale-MAE achieved the highest values across all metrics and both change detection strategies, followed by the UNet baseline (trained from scratch) for the concatenation

strategy and DOFA for the differencing strategy. Although falling short of the top two performances, SatlasNet and GFM also outperformed the rest of their competitors, achieving IoU values exceeding 20 for both strategies. The change detection strategy only had a minor effect on performance, except for UNet which achieved top-performance using concatenation and lowest performance among the models using differencing. In general, our change detection strategy results indicate that the preferable strategy is model-specific.

Table A16 compares the change detection results on the SpaceNet 7 dataset obtained using the concatenation strategy and 100% of the labeled training data to limited label scenarios, using only 50% and 10% of the labeled training data. In the 50% scenario, several foundation models (DOFA, Prithvi, and RemoteCLIP), as well as the UNet baseline, experienced large performance drops (> 5 mIoU) compared to the 100% scenario. In comparison, other foundation models successfully retained most of their performance. For example, the performance of GFM, SatlasNet, and Scale-MAE dropped by less than 2 mIoU points. However, none of the models managed to retain their performance from the 100% scenario when reducing the labeled training data to 10%.

Overall, we attribute the strong performance of Scale-MAE on the SpaceNet 7 change detection task to its robust multi-scale representations, especially since the satellite imagery in SpaceNet 7 (Planet) has a higher spatial resolution (approx. 4 m) than the Sentinel imagery typically used for pertaining foundation models. Furthermore, many other foundation models do not specifically address scale-specific information in satellite images [15]. It is also noteworthy that most foundation models, including Scale-MAE, were not specifically designed to solve change detection tasks.

## J. AI4SmallFarms

The AI4SmallFarms dataset consists of 439,001 manually annotated agricultural field polygons distributed across 62 non-overlapping tiles, each approximately 5 × 5 km, in Vietnam and Cambodia. The dataset includes multi-temporal Sentinel-2 image composites, created by aggregating monthly cloud-free images using median pixel values. The Sentinel-2 bands B2, B3, B4 and B8 are included for each tile. Additionally, high-resolution Google Map (GM) images are incorporated to improve boundary accuracy; however, these are not included in training the models, as the goal is to be able to perform farm boundary delineation from Sentinel-2 imagery. The digitization process involved detailed manual annotation from both Sentinel-2 and GM images, followed by a rigorous multi-step quality control procedure that included topological consistency checks and independent verification by multiple quality control teams. Fig. A10 displays an example sample.

1) *Extensive Results:* The results on the AI4SmallFarms dataset, presented in Table A17, show that the fully supervised UNet model significantly outperforms all evaluated GFM. The UNet achieves an mIoU of 46.34% and a mean F1 score of 62.36%, while the GFM exhibit mIoU values ranging from 21.47% to 27.19% and m-f1 scores between 35.07% and 42.73%. This performance gap suggests that being
<figure><img src="image_18.png" /><figcaption>Fig. A9: Example data of **SpaceNet** (Location: Global. Domain: Urban.). The pre-change and post-change images represent the first and last cloud-free satellite images in the time series. The label visualizes unchanged (black) and changed (white) pixels regarding built-up areas between the two images.</figcaption></figure>

<caption>Fig. A10: Example data of **AI4SmallFarms** (Location: Southeast Asia. Domain: Agriculture.). The example image is taken from Cambodia and visualized with B4, B3 and B2 bands.</caption>

<table><caption>TABLE A15: Change detection results for the SpaceNet 7 dataset with 100 % of the data. The results in the last two columns are for the foreground (change) class. The best results are in **bold** and the second best **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">mIoU ↑</th><th colspan="2">m-F1 ↑</th><th colspan="2">m-acc ↑</th><th colspan="2">IoU ↑</th><th colspan="2">F1 ↑</th></tr><tr><th>Conc.</th><th>Diff.</th><th>Conc.</th><th>Diff.</th><th>Conc.</th><th>Diff.</th><th>Conc.</th><th>Diff.</th><th>Conc.</th><th>Diff.</th></tr></thead><tbody><tr><td>CROMA</td><td>59.28</td><td>59.85</td><td>66.04</td><td>66.77</td><td>99.14</td><td>99.28</td><td>19.41</td><td>20.41</td><td>32.52</td><td>33.91</td></tr><tr><td>DOFA</td><td>61.84</td><td><u>61.47</u></td><td>69.37</td><td><u>68.93</u></td><td><strong>99.45</strong></td><td><u>99.35</u></td><td>24.24</td><td><u>23.60</u></td><td>39.02</td><td><u>38.19</u></td></tr><tr><td>GFM-Swin</td><td>60.89</td><td>60.53</td><td>68.17</td><td>67.69</td><td>99.32</td><td>99.32</td><td>22.46</td><td>21.75</td><td>36.68</td><td>35.72</td></tr><tr><td>Prithvi</td><td>56.54</td><td>58.67</td><td>62.09</td><td>65.12</td><td>99.00</td><td>99.26</td><td>14.09</td><td>18.08</td><td>24.69</td><td>30.62</td></tr><tr><td>RemoteCLIP</td><td>57.76</td><td>58.40</td><td>63.76</td><td>64.74</td><td>99.33</td><td>99.25</td><td>16.18</td><td>17.55</td><td>27.86</td><td>29.87</td></tr><tr><td>SatlasNet</td><td>61.88</td><td>60.63</td><td>69.44</td><td>67.83</td><td>99.38</td><td>99.31</td><td>24.38</td><td>21.96</td><td>39.20</td><td>36.01</td></tr><tr><td>Scale-MAE</td><td><strong>62.96</strong></td><td><strong>62.62</strong></td><td><strong>70.79</strong></td><td><strong>70.39</strong></td><td><strong>99.45</strong></td><td><strong>99.39</strong></td><td><strong>26.47</strong></td><td><strong>25.85</strong></td><td><strong>41.86</strong></td><td><strong>41.09</strong></td></tr><tr><td>SpectralGPT</td><td>58.86</td><td>59.43</td><td>65.41</td><td>66.20</td><td>99.25</td><td>99.26</td><td>18.48</td><td>19.60</td><td>31.19</td><td>32.78</td></tr><tr><td>S12-MoCo</td><td>57.64</td><td>58.28</td><td>63.71</td><td>64.63</td><td>99.08</td><td>99.12</td><td>16.20</td><td>17.44</td><td>27.89</td><td>29.70</td></tr><tr><td>S12-DINO</td><td>56.47</td><td>57.00</td><td>61.95</td><td>62.77</td><td>99.05</td><td>99.04</td><td>13.89</td><td>14.96</td><td>24.39</td><td>26.03</td></tr><tr><td>S12-MAE</td><td>57.13</td><td>57.50</td><td>62.96</td><td>63.50</td><td>99.06</td><td>99.09</td><td>15.20</td><td>15.91</td><td>26.39</td><td>27.45</td></tr><tr><td>S12-Data2Vec</td><td>58.23</td><td>57.89</td><td>64.50</td><td>64.04</td><td>99.23</td><td>99.15</td><td>17.22</td><td>16.62</td><td>29.39</td><td>28.51</td></tr><tr><td>UNet Baseline</td><td><u>62.09</u></td><td>51.01</td><td><u>69.70</u></td><td>54.36</td><td><u>99.41</u></td><td>96.48</td><td><u>24.76</u></td><td>5.55</td><td><u>39.70</u></td><td>10.51</td></tr><tr><td>ViT Baseline</td><td>52.57</td><td>57.09</td><td>55.78</td><td>62.92</td><td>98.62</td><td>99.00</td><td>6.53</td><td>15.17</td><td>12.25</td><td>26.35</td></tr></tbody></table>

<table><caption>TABLE A16: Change detection results for the SpaceNet 7 dataset with the concatenation strategy and limited training label scenarios. The best results are in **bold** and the second best **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="3">mIoU ↑</th><th colspan="3">m-F1 ↑</th><th colspan="3">m-acc ↑</th></tr><tr><th>10 %</th><th>50 %</th><th>100 %</th><th>10 %</th><th>50 %</th><th>100 %</th><th>10 %</th><th>50 %</th><th>100 %</th></tr></thead><tbody><tr><td>CROMA</td><td>42.15</td><td>59.31</td><td>59.28</td><td>46.74</td><td>66.07</td><td>66.04</td><td>82.90</td><td>99.20</td><td>99.14</td></tr><tr><td>DOFA</td><td>46.10</td><td>47.06</td><td>61.84</td><td>48.09</td><td>49.95</td><td>69.37</td><td>92.03</td><td>92.07</td><td><strong>99.45</strong></td></tr><tr><td>GFM-Swin</td><td>39.48</td><td>59.83</td><td>60.89</td><td>44.58</td><td>66.77</td><td>68.17</td><td>78.31</td><td>99.24</td><td>99.32</td></tr><tr><td>Prithvi</td><td>36.78</td><td>49.45</td><td>56.54</td><td>42.88</td><td>52.24</td><td>62.09</td><td>72.87</td><td>95.36</td><td>99.00</td></tr><tr><td>RemoteCLIP</td><td>43.11</td><td>50.83</td><td>57.76</td><td>46.97</td><td>53.66</td><td>63.76</td><td>85.29</td><td>97.04</td><td>99.33</td></tr><tr><td>SatlasNet</td><td><strong>49.78</strong></td><td><u>60.74</u></td><td>61.88</td><td><u>49.89</u></td><td>67.97</td><td>69.44</td><td><strong>99.55</strong></td><td><u>99.31</u></td><td>99.38</td></tr><tr><td>Scale-MAE</td><td><u>49.68</u></td><td><strong>61.24</strong></td><td><strong>62.96</strong></td><td><strong>52.10</strong></td><td><strong>68.61</strong></td><td><strong>70.79</strong></td><td><u>96.19</u></td><td><strong>99.36</strong></td><td><strong>99.45</strong></td></tr><tr><td>SpectralGPT</td><td>36.31</td><td>56.28</td><td>58.86</td><td>42.74</td><td>61.82</td><td>65.41</td><td>71.68</td><td>98.76</td><td>99.25</td></tr><tr><td>S12-MoCo</td><td>49.46</td><td>56.21</td><td>57.64</td><td>50.16</td><td>61.63</td><td>63.71</td><td>98.33</td><td>98.92</td><td>99.08</td></tr><tr><td>S12-DINO</td><td>41.15</td><td>55.14</td><td>56.47</td><td>45.71</td><td>60.00</td><td>61.95</td><td>81.51</td><td>98.81</td><td>99.05</td></tr><tr><td>S12-MAE</td><td>40.51</td><td>55.55</td><td>57.13</td><td>45.30</td><td>60.63</td><td>62.96</td><td>80.27</td><td>98.85</td><td>99.06</td></tr><tr><td>S12-Data2Vec</td><td>40.66</td><td>56.94</td><td>58.23</td><td>45.44</td><td>62.67</td><td>64.50</td><td>80.49</td><td>99.07</td><td>99.23</td></tr><tr><td>UNet Baseline</td><td>46.08</td><td>46.82</td><td><u>62.09</u></td><td>49.49</td><td>50.06</td><td><u>69.70</u></td><td>90.02</td><td>91.24</td><td><strong>99.41</strong></td></tr><tr><td>ViT Baseline</td><td>36.01</td><td>49.21</td><td>52.57</td><td>42.44</td><td>51.45</td><td>55.78</td><td>71.22</td><td>95.85</td><td>98.62</td></tr></tbody></table>
<table><caption>TABLE A17: Results for the AI4SmallFarms dataset. Best results in **bold**, second best **underlined**.</caption><thead><tr><th rowspan="2">Model</th><th colspan="2">IoU ↑</th><th colspan="2">F1 ↑</th><th colspan="2">prec ↑</th><th colspan="2">rec ↑</th><th rowspan="2">acc ↑</th></tr><tr><th>mIoU</th><th>fg-IoU</th><th>m-F1</th><th>fg-F1</th><th>m-prec</th><th>fg-prec</th><th>m-rec</th><th>fg-rec</th><th>m-acc</th></tr></thead><tbody><tr><td>CROMA</td><td>25.65</td><td><u>26.54</u></td><td>40.82</td><td><u>41.95</u></td><td><u>56.33</u></td><td><u>86.83</u></td><td>56.69</td><td>27.65</td><td>40.84</td></tr><tr><td>DOFA</td><td>27.07</td><td>26.18</td><td>42.60</td><td>41.50</td><td>56.11</td><td>82.69</td><td>55.82</td><td><u>27.70</u></td><td>42.62</td></tr><tr><td>GFM-Swin</td><td>27.19</td><td>25.44</td><td>42.73</td><td>40.56</td><td>55.09</td><td>79.27</td><td>54.65</td><td>27.25</td><td>42.81</td></tr><tr><td>Prithvi</td><td>26.86</td><td>25.79</td><td>42.34</td><td>41.01</td><td>55.50</td><td>81.38</td><td>55.19</td><td>27.41</td><td>42.37</td></tr><tr><td>RemoteCLIP</td><td>25.12</td><td>26.24</td><td>40.14</td><td>41.57</td><td>55.76</td><td>86.46</td><td>56.18</td><td>27.36</td><td>40.17</td></tr><tr><td>SatlasNet</td><td>25.13</td><td>25.29</td><td>40.17</td><td>40.37</td><td>54.35</td><td>82.27</td><td>54.39</td><td>26.74</td><td>40.17</td></tr><tr><td>Scale-MAE</td><td>21.47</td><td>26.43</td><td>35.07</td><td>41.81</td><td>55.30</td><td><b>93.76</b></td><td>58.06</td><td>26.91</td><td>35.77</td></tr><tr><td>SpectralGPT</td><td>26.75</td><td>25.22</td><td>42.19</td><td>40.27</td><td>54.66</td><td>79.11</td><td>54.30</td><td>27.01</td><td>42.25</td></tr><tr><td>S12-Data2Vec</td><td>24.23</td><td>25.25</td><td>39.00</td><td>40.32</td><td>54.06</td><td>83.67</td><td>54.33</td><td>26.56</td><td>39.03</td></tr><tr><td>S12-DINO</td><td>25.62</td><td>25.04</td><td>40.79</td><td>40.05</td><td>54.12</td><td>80.35</td><td>53.98</td><td>26.67</td><td>40.80</td></tr><tr><td>S12-MAE</td><td>24.69</td><td>24.94</td><td>39.60</td><td>39.93</td><td>53.73</td><td>81.54</td><td>53.78</td><td>26.44</td><td>39.60</td></tr><tr><td>S12-MoCo</td><td>25.38</td><td>25.47</td><td>40.48</td><td>40.59</td><td>54.67</td><td>82.62</td><td>54.70</td><td>26.91</td><td>40.48</td></tr><tr><td>UNet Baseline</td><td><b>46.34</b></td><td><b>34.07</b></td><td><b>62.36</b></td><td><b>50.83</b></td><td><b>67.82</b></td><td>71.61</td><td><b>63.38</b></td><td><b>39.40</b></td><td><b>65.90</b></td></tr><tr><td>ViT Baseline</td><td><u>38.37</u></td><td>12.71</td><td><u>50.31</u></td><td>22.56</td><td>50.46</td><td>20.23</td><td>50.54</td><td>25.49</td><td><u>65.81</u></td></tr></tbody></table>

able to train the encoder is beneficial for capturing the fine-grained spatial details required for accurate field boundary segmentation in medium-resolution optical imagery. In contrast, the GFM's may lack the necessary spatial resolution or fail to effectively generalize the specific characteristics of smallholder farms in Vietnam and Cambodia. These findings highlight the importance of task-specific model design and suggest that, for certain specialized applications, fully supervised models may offer superior performance over more generalized pre-trained models.

### K. BioMassters

BioMassters is an Above Ground forest Biomass (AGB) estimation dataset at 10m spatial resolution, covering 8.5 million hectares of Finland's boreal forests. The dataset consists of 13,000 reference AGB tiles, each of size 256 × 256 pixels, along with corresponding time series images from Sentinel-1 and Sentinel-2 satellites. The reference AGB data is derived from Airborne LiDAR campaigns conducted between 2017 and 2021 by the Finland Forest Centre and the National Land Survey (NLS) of Finland. The input data includes 12-month time series of Sentinel-1 and Sentinel-2. The Sentinel-1 input data contains both ascending and descending orbit signals, resulting in four bands in each image: ASC VV, ASC VH, DSC VV, and DSC VH. The Sentinel-2 images contain 10 bands, B2-B8, B8a, B11, and B12. The training and validation set is created using 80-20 split. The test set is created by randomly selecting 30 percent data points from 2018, 2020, and 2021, and 20 percent from 2019. Fig. A11 shows a data sample. However, it is important to consider that the dataset is localized only in Finland.

1) **Extensive Results:** The detailed results on BioMassters datasets are presented in Table A18. The evaluation is conducted under different temporal strategies, data modalities and limited label strategies.

Table A18 contains evaluation results on all GFM's with pre-trained encoder on representation learning and decoder fine-tuned to estimate forest biomass as a regression task. The results show that the biomass estimation is more accurate with multi-temporal data than uni-temporal data. The estimation

is further improved with the L-TAE multi-temporal feature learning strategy. This observation is consistent for all listed GFM's. In terms of modality, we see that the task is better learned through optical modality. In fact, in most of the cases, models solely trained with optical data achieved better results than combined SAR and optical data. We tested GFM's with limited labels to replicate real world application scenarios, where label scarcity is often an issue. The obtained results show that training with 100% labels provides better results than training with 50% which is better than training with 10% dataset, reflecting the general trend of getting better results with more training labels. However, the results also indicate that using 50% labels might be sufficient as training with 50% labels provides almost similar estimations as training with 100% labels. The best results are obtained from SpectralGPT and CROMA (optical) GFM whereas the worst results are from RemoteCLIP.

We compared the GFM results with two basic supervised baselines; U-Net and ViT. Although not by a big margin but the supervised U-Net baselines outperformed all GFM's, raising questions on the generalizability of GFM for biomass estimation tasks.

Table A19 lists all the datasets used to evaluate the surveyed GFM's in their original publication. Every GFM uses its own protocol on a specific set of tasks, with few overlaps. Change detection and regression are barely evaluated.

In this section of the Appendix, we present additional results that explore patterns between model performance and various characteristics of both pre-training and downstream datasets. Specifically, we investigate how performance varies across domains, geographic regions, and sensor resolutions, providing further insight into the factors that influence the generalization ability of geospatial foundation models.
<figure><img src="image_19.png" /><figcaption>Fig. A11: Example data of **BioMassters** (Location: Finland. Domain: Forest.). Sentinel-1 images are visualized with VV, VH, VV bands, and the Sentinel-2 images with B4, B3, and B2 bands. The images are from from peak summer month (August).</figcaption></figure>

<table><caption>TABLE A18: Biomasters results. The evaluation metric is Root Mean Square Error (RMSE ↓). Best results are highlighted in bold, and second best are underlined. SpectralGPT and CROMA are the top-performing GFMs. Overall multi-temporal with L-TAE is the best configuration.</caption><thead><tr><th rowspan="3">Model</th><th rowspan="3">Modality</th><th colspan="3">Uni-temporal</th><th colspan="6">Multi-temporal</th></tr><tr><th rowspan="2">10%</th><th rowspan="2">50%</th><th rowspan="2">100%</th><th colspan="2">10%</th><th colspan="2">50%</th><th colspan="2">100%</th></tr><tr><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th><th>Linear</th><th>L-TAE</th></tr></thead><tbody><tr><td>CROMA</td><td>joint</td><td>45.88</td><td>43.74</td><td>43.07</td><td>43.24</td><td>41.19</td><td>41.04</td><td>38.50</td><td>40.24</td><td>37.86</td></tr><tr><td>CROMA</td><td>optical</td><td>46.99</td><td>44.88</td><td>44.16</td><td>44.11</td><td>40.25</td><td>40.80</td><td>38.76</td><td>39.99</td><td>36.81</td></tr><tr><td>CROMA</td><td>sar</td><td>51.43</td><td>49.82</td><td>49.19</td><td>46.21</td><td>44.07</td><td>44.22</td><td>41.86</td><td>43.29</td><td>41.45</td></tr><tr><td>DOFA</td><td>joint</td><td>49.29</td><td>46.57</td><td>45.87</td><td>46.89</td><td>46.03</td><td>44.27</td><td>44.05</td><td>43.25</td><td>42.81</td></tr><tr><td>GFM-Swin</td><td>optical</td><td>52.98</td><td>51.34</td><td>50.46</td><td>50.24</td><td>49.30</td><td>48.55</td><td>48.19</td><td>47.96</td><td>46.83</td></tr><tr><td>Prithvi</td><td>optical</td><td>48.93</td><td>46.76</td><td>46.05</td><td>47.58</td><td>45.48</td><td>44.04</td><td>41.03</td><td>42.90</td><td>39.99</td></tr><tr><td>RemoteCLIP</td><td>optical</td><td>55.14</td><td>53.58</td><td>52.81</td><td>53.22</td><td>53.32</td><td>50.65</td><td>50.09</td><td>49.60</td><td>49.79</td></tr><tr><td>SatlasNet</td><td>optical</td><td>47.19</td><td>45.80</td><td>45.18</td><td>45.90</td><td>44.38</td><td>42.87</td><td>42.23</td><td>42.13</td><td>41.67</td></tr><tr><td>Scale-MAE</td><td>optical</td><td>69.53</td><td>67.27</td><td>66.55</td><td>63.19</td><td>54.16</td><td>53.94</td><td>46.74</td><td>52.24</td><td>47.15</td></tr><tr><td>SpectralGPT</td><td>optical</td><td>46.36</td><td>44.01</td><td>43.35</td><td>42.01</td><td>39.44</td><td>39.56</td><td>37.34</td><td>38.55</td><td>36.11</td></tr><tr><td>S12-MoCo</td><td>optical</td><td>47.89</td><td>46.29</td><td>45.69</td><td>44.27</td><td>44.83</td><td>42.48</td><td>41.08</td><td>41.82</td><td>40.21</td></tr><tr><td>S12-DINO</td><td>optical</td><td>47.65</td><td>45.98</td><td>45.39</td><td>43.91</td><td>42.74</td><td>42.13</td><td>41.47</td><td>41.54</td><td>41.23</td></tr><tr><td>S12-MAE</td><td>optical</td><td>48.21</td><td>46.35</td><td>45.74</td><td>45.73</td><td>43.76</td><td>43.27</td><td>41.66</td><td>42.54</td><td>41.07</td></tr><tr><td>S12-MAE</td><td>sar</td><td>54.41</td><td>52.44</td><td>51.69</td><td>50.43</td><td>58.62</td><td>47.88</td><td>50.88</td><td>46.87</td><td>50.09</td></tr><tr><td>S12-Data2Vec</td><td>optical</td><td>46.38</td><td>46.20</td><td>45.48</td><td>44.86</td><td>42.02</td><td>42.76</td><td>42.82</td><td>42.02</td><td>41.91</td></tr><tr><td>UNet Baseline</td><td>optical</td><td>44.83</td><td>41.24</td><td>40.83</td><td>40.39</td><td></td><td>36.72</td><td></td><td>35.67</td><td></td></tr><tr><td>ViT Baseline</td><td>optical</td><td>49.13</td><td>46.22</td><td>43.77</td><td>46.19</td><td>44.89</td><td>39.96</td><td>39.56</td><td>40.94</td><td>38.55</td></tr></tbody></table>
<table><caption>TABLE A19: Datasets used to evaluate GFMs in their original publications. Every model is evaluated through its protocol.</caption><thead><tr><th>Dataset</th><th>Task</th><th>CROMA</th><th>DOFA</th><th>GFM</th><th>Prithvi</th><th>Remote CLIP</th><th>Satlas</th><th>Scale-MAE</th><th>Spectral GPT</th><th>S12-*</th></tr></thead><tbody><tr><td>BigEarthNet</td><td>Cla.</td><td>✓</td><td></td><td>✓</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td>✓</td></tr><tr><td>EuroSAT</td><td>Cla.</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>So2Sat</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>UC Merced</td><td>Cla.</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Canadian Cropland</td><td>Cla.</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GEO-Bench-class</td><td>Cla.</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>AiRound</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>CV-BrCT</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>MLRSNet</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>OPTIMAL-31</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>WHU-RS19</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>RESISC45</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>AID</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>fMoW</td><td>Cla.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>fMoW-Sentinel</td><td>Cla.</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DFC2020</td><td>Seg.</td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td>✓</td></tr><tr><td>iSAID</td><td>Seg.</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Vaihingen</td><td>Seg.</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Potsdam</td><td>Seg.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>LoveDA</td><td>Seg.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>MARIDA</td><td>Seg.</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>DW-Expert</td><td>Seg.</td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>GEO-Bench-seg</td><td>Seg.</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>WHU Aerial</td><td>Seg.</td><td></td><td></td><td>✓</td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Sen1Floods11</td><td>Seg.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>HLS Burn Scars</td><td>Seg.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Mass Roads</td><td>Seg.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>Mass Buildings</td><td>Seg.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>HLS Multi-Temp. Crop</td><td>Seg.</td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>SpaceNet-1</td><td>Seg.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td>SegMunich</td><td>Seg.</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>RSICD</td><td>Ret.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>RSITMD</td><td>Ret.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>UCM</td><td>Ret.</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>DOTA</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td>✓</td><td></td><td></td><td></td></tr><tr><td>DIOR</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>HRRSD</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>RSOD</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>LEVIR</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>HSRC</td><td>Det.</td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td></tr><tr><td>Airbus Ships</td><td>Det.</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td></tr><tr><td>OSCD</td><td>CD</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>DSFIN</td><td>CD</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>SpaceNet-2</td><td>SR</td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cloud Gap</td><td>Imp.</td><td></td><td></td><td></td><td>✓</td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>
<figure><img src="image_20.png" /><figcaption>Fig. A12: Performance variation across domains for different GFM s. The heatmap illustrates relative performance deltas (higher is better) compared to the domain-specific average. Warmer colors indicate higher-than-average performance, while cooler colors denote underperformance. This visualization highlights the modality-task and domain-specific disparities across models, including performance gaps such as GFM-Swin's relative drop in Flood and Marine tasks, potentially due to distributional shifts in pretraining.</figcaption></figure>

<figure><img src="image_21.png" /><figcaption>Fig. A13: Geographic variation in model performance across foundation models. This heatmap displays average performance scores across five geographic regions. Each cell reflects the model's absolute performance within a specific region, highlighting spatial generalization capabilities. These patterns underscore the importance of geographic context in evaluating GFM s.</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>Fig. A14: Performance variation of GFM across different sensors and resolutions. This heatmap illustrates how model performance shifts depending on the source of input imagery, categorized by sensor types and their associated resolutions. The differences highlight the sensitivity of GFM to input resolution and sensor characteristics.</figcaption></figure>